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ACOSTA-MENDOZA, NIUSVEL, ALICIA MORALES-REYES, HUGO JAIR ESCALANTE, and ANDRÉS GAGO-ALONSO. "LEARNING TO ASSEMBLE CLASSIFIERS VIA GENETIC PROGRAMMING." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 07 (October 14, 2014): 1460005. http://dx.doi.org/10.1142/s0218001414600052.
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This paper introduces a novel approach for building heterogeneous ensembles based on genetic programming (GP). Ensemble learning is a paradigm that aims at combining individual classifier's outputs to improve their performance. Commonly, classifiers outputs are combined by a weighted sum or a voting strategy. However, linear fusion functions may not effectively exploit individual models' redundancy and diversity. In this research, a GP-based approach to learn fusion functions that combine classifiers outputs is proposed. Heterogeneous ensembles are aimed in this study, these models use individual classifiers which are based on different principles (e.g. decision trees and similarity-based techniques). A detailed empirical assessment is carried out to validate the effectiveness of the proposed approach. Results show that the proposed method is successful at building very effective classification models, outperforming alternative ensemble methodologies. The proposed ensemble technique is also applied to fuse homogeneous models' outputs with results also showing its effectiveness. Therefore, an in-depth analysis from different perspectives of the proposed strategy to build ensembles is presented with a strong experimental support.
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Reddy, S. Pavan Kumar, and U. Sesadri. "A Bootstrap Aggregating Technique on Link-Based Cluster Ensemble Approach for Categorical Data Clustering." INTERNATIONAL JOURNAL OF COMPUTERS & TECHNOLOGY 10, no. 8 (August 30, 2013): 1913–21. http://dx.doi.org/10.24297/ijct.v10i8.1468.
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Although attempts have been made to solve the problem of clustering categorical data via cluster ensembles, with the results being competitive to conventional algorithms, it is observed that these techniques unfortunately generate a final data partition based on incomplete information. The underlying ensemble-information matrix presents only cluster-data point relations, with many entries being left unknown. The paper presents an analysis that suggests this problem degrades the quality of the clustering result, and it presents a BSA (Bootstrap Aggregation) is a machine learning ensemble meta-algorithm designed to improve the stability and accuracy along with a new link-based approach, which improves the conventional matrix by discovering unknown entries through similarity between clusters in an ensemble. In particular, an efficient BSA and link-based algorithm is proposed for the underlying similarity assessment. Afterward, to obtain the final clustering result, a graph partitioning technique is applied to a weighted bipartite graph that is formulated from the refined matrix. Experimental results on multiple real data sets suggest that the proposed link-based method almost always outperforms both conventional clustering algorithms for categorical data and well-known cluster ensemble techniques.
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Goyal, Jyotsana. "IMPROVING CLASSIFICATION PERFORMANCE USING ENSEMBLE LEARNING APPROACH." BSSS Journal of Computer 14, no. 1 (June 30, 2023): 63–75. http://dx.doi.org/10.51767/jc1409.
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The data mining techniques are used for evaluation of the data in order to find and represent the data in such manner by which the applications are becomes beneficial. Therefore, different kinds of computational algorithms and modeling’s are incorporated for analyzing the data. These computational algorithms are help to understand the data patterns and their application utility. The data mining algorithms supports supervised as well as unsupervised techniques of data analysis. This work is aimed to investigate about the supervised learning technique specifically performance improvements on classification techniques. The proposed classification model includes the multiple classifiers namely Bayesian classifier, k-nearest neighbor and the c4.5 decision tree algorithm. By nature of the outcomes and the modeling of the data these algorithms are functioning differently from each other. Thus, a weight based classification technique is introduced in this work. The weight is a combination of outcomes provided by the implemented three classifiers in terms of their predicted class labels. Using the weighted outcomes, the final class label for the input data instance is decided. The implementation of the proposed working model is performed with the help of JAVA and WEKA classes. The results obtained by experimentation of the proposed approach with the vehicle data set demonstrate the high accurate classification results. Thus, the proposed model is an effective classification technique as compared to single model implementation for classification task.
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Cawood, Pieter, and Terence Van Zyl. "Evaluating State-of-the-Art, Forecasting Ensembles and Meta-Learning Strategies for Model Fusion." Forecasting 4, no. 3 (August 18, 2022): 732–51. http://dx.doi.org/10.3390/forecast4030040.
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The techniques of hybridisation and ensemble learning are popular model fusion techniques for improving the predictive power of forecasting methods. With limited research that instigates combining these two promising approaches, this paper focuses on the utility of the Exponential Smoothing-Recurrent Neural Network (ES-RNN) in the pool of base learners for different ensembles. We compare against some state-of-the-art ensembling techniques and arithmetic model averaging as a benchmark. We experiment with the M4 forecasting dataset of 100,000 time-series, and the results show that the Feature-Based FORecast Model Averaging (FFORMA), on average, is the best technique for late data fusion with the ES-RNN. However, considering the M4’s Daily subset of data, stacking was the only successful ensemble at dealing with the case where all base learner performances were similar. Our experimental results indicate that we attain state-of-the-art forecasting results compared to Neural Basis Expansion Analysis (N-BEATS) as a benchmark. We conclude that model averaging is a more robust ensembling technique than model selection and stacking strategies. Further, the results show that gradient boosting is superior for implementing ensemble learning strategies.
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Lenin, Thingbaijam, and N. Chandrasekaran. "Learning from Imbalanced Educational Data Using Ensemble Machine Learning Algorithms." Webology 18, Special Issue 01 (April 29, 2021): 183–95. http://dx.doi.org/10.14704/web/v18si01/web18053.
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Student’s academic performance is one of the most important parameters for evaluating the standard of any institute. It has become a paramount importance for any institute to identify the student at risk of underperforming or failing or even drop out from the course. Machine Learning techniques may be used to develop a model for predicting student’s performance as early as at the time of admission. The task however is challenging as the educational data required to explore for modelling are usually imbalanced. We explore ensemble machine learning techniques namely bagging algorithm like random forest (rf) and boosting algorithms like adaptive boosting (adaboost), stochastic gradient boosting (gbm), extreme gradient boosting (xgbTree) in an attempt to develop a model for predicting the student’s performance of a private university at Meghalaya using three categories of data namely demographic, prior academic record, personality. The collected data are found to be highly imbalanced and also consists of missing values. We employ k-nearest neighbor (knn) data imputation technique to tackle the missing values. The models are developed on the imputed data with 10 fold cross validation technique and are evaluated using precision, specificity, recall, kappa metrics. As the data are imbalanced, we avoid using accuracy as the metrics of evaluating the model and instead use balanced accuracy and F-score. We compare the ensemble technique with single classifier C4.5. The best result is provided by random forest and adaboost with F-score of 66.67%, balanced accuracy of 75%, and accuracy of 96.94%.
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Arora, Madhur, Sanjay Agrawal, and Ravindra Patel. "Machine Learning Technique for Predicting Location." International Journal of Electrical and Electronics Research 11, no. 2 (June 30, 2023): 639–45. http://dx.doi.org/10.37391/ijeer.110254.
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In the current era of internet and mobile phone usage, the prediction of a person's location at a specific moment has become a subject of great interest among researchers. As a result, there has been a growing focus on developing more effective techniques to accurately identify the precise location of a user at a given instant in time. The quality of GPS data plays a crucial role in obtaining high-quality results. Numerous algorithms are available that leverage user movement patterns and historical data for this purpose. This research presents a location prediction model that incorporates data from multiple users. To achieve the most accurate predictions, regression techniques are utilized for user trajectory prediction, and ensemble algorithmic procedures, such as the random forest approach, the Adaboost method, and the XGBoost method, are employed. The primary goal is to improve prediction accuracy. The improvement accuracy of proposed ensemble method is around 21.2%decrease in errors, which is much greater than earlier systems that are equivalent. Compared to previous comparable systems, the proposed system demonstrates an approximately 15% increase in accuracy when utilizing the ensemble methodology.
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Rahimi, Nouf, Fathy Eassa, and Lamiaa Elrefaei. "An Ensemble Machine Learning Technique for Functional Requirement Classification." Symmetry 12, no. 10 (September 25, 2020): 1601. http://dx.doi.org/10.3390/sym12101601.
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In Requirement Engineering, software requirements are classified into two main categories: Functional Requirement (FR) and Non-Functional Requirement (NFR). FR describes user and system goals. NFR includes all constraints on services and functions. Deeper classification of those two categories facilitates the software development process. There are many techniques for classifying FR; some of them are Machine Learning (ML) techniques, and others are traditional. To date, the classification accuracy has not been satisfactory. In this paper, we introduce a new ensemble ML technique for classifying FR statements to improve their accuracy and availability. This technique combines different ML models and uses enhanced accuracy as a weight in the weighted ensemble voting approach. The five combined models are Naïve Bayes, Support Vector Machine (SVM), Decision Tree, Logistic Regression, and Support Vector Classification (SVC). The technique was implemented, trained, and tested using a collected dataset. The accuracy of classifying FR was 99.45%, and the required time was 0.7 s.
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., Hartono, Opim Salim Sitompul, Erna Budhiarti Nababan, Tulus ., Dahlan Abdullah, and Ansari Saleh Ahmar. "A New Diversity Technique for Imbalance Learning Ensembles." International Journal of Engineering & Technology 7, no. 2.14 (April 8, 2018): 478. http://dx.doi.org/10.14419/ijet.v7i2.11251.
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Data mining and machine learning techniques designed to solve classification problems require balanced class distribution. However, in reality sometimes the classification of datasets indicates the existence of a class represented by a large number of instances whereas there are classes with far fewer instances. This problem is known as the class imbalance problem. Classifier Ensembles is a method often used in overcoming class imbalance problems. Data Diversity is one of the cornerstones of ensembles. An ideal ensemble system should have accurrate individual classifiers and if there is an error it is expected to occur on different objects or instances. This research will present the results of overview and experimental study using Hybrid Approach Redefinition (HAR) Method in handling class imbalance and at the same time expected to get better data diversity. This research will be conducted using 6 datasets with different imbalanced ratios and will be compared with SMOTEBoost which is one of the Re-Weighting method which is often used in handling class imbalance. This study shows that the data diversity is related to performance in the imbalance learning ensembles and the proposed methods can obtain better data diversity.
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Teoh, Chin-Wei, Sin-Ban Ho, Khairi Shazwan Dollmat, and Chuie-Hong Tan. "Ensemble-Learning Techniques for Predicting Student Performance on Video-Based Learning." International Journal of Information and Education Technology 12, no. 8 (2022): 741–45. http://dx.doi.org/10.18178/ijiet.2022.12.8.1679.
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The transformation of education norms from face-to-face teaching era to the Massive Open Online Courses (MOOCs) era has promoted the rise of the big data era in educational data. This situation has created an opportunity for an educator to utilize the available data from MOOCs to facilitate student learning and performance. Therefore, this research study aims to introduce three types of ensemble learning methods, which are stacking, boosting, and bagging, to predict student performance. These techniques combine the advantage of feature selection method and Synthetic Minority Oversampling Technique (SMOTE) algorithm as a method to balance the number of output features to build the ensemble learning model. As a result, the proposed AdaBoost type ensemble classifier has shown the highest prediction accuracy of more than 90% and Area Under the Curve (AUC) of approximately 0.90. Results by AdaBoost classifier have outperformed other ensemble classifiers, stacking and bagging as well as base classifiers.
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Hussein, Salam Allawi, Alyaa Abduljawad Mahmood, and Emaan Oudah Oraby. "Network Intrusion Detection System Using Ensemble Learning Approaches." Webology 18, SI05 (October 30, 2021): 962–74. http://dx.doi.org/10.14704/web/v18si05/web18274.
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To mitigate modern network intruders in a rapidly growing and fast pattern changing network traffic data, single classifier is not sufficient. In this study Chi-Square feature selection technique is used to select the most important features of network traffic data, then AdaBoost, Random Forest (RF), and XGBoost ensemble classifiers were used to classify data based on binary-classes and multi-classes. The aim of this study is to improve detection rate accuracy for every individual attack types and all types of attacks, which will help us to identify attacks and particular category of attacks. The proposed method is evaluated using k-fold cross validation, and the experimental results of all the three classifiers with and without feature selection are compared together. We used two different datasets in our experiments to evaluate the model performance. The used datasets are NSL-KDD and UNSW-NB15.
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P A, Sadiyamole, and Dr Manju Priya S. "Heart Disease Prediction Using Ensemble Stacking Technique." International Journal of Engineering Research in Computer Science and Engineering 9, no. 8 (August 6, 2022): 19–24. http://dx.doi.org/10.36647/ijercse/09.08.art004.
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Heart disease is one of the critical reasons behind the majority of the human loss.Heart failure has proven as the major health issue in both men and women.This causes human life very dreadful.Diagnosing heart issues in advance is a tedious task as it requires enormous amount of clinical tests.Data mining techniques like machine learning and deep learning have proven to be fruitful in making decisions and diagnose various diseases in advance.In this paper,various machine learning techniques have been used along with stacking ensemble method that focus to improve the prediction of heart failure.The accuracy of diagnosis is very important in the case of heart disease.Due to the inadequacy of prediction and diagnosis, traditional approaches fail to discover various heart failures. Health care organizations collect heart data sets which can be used to apply machine learning models for prognosis.
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Zubair Khan, Mohammad. "Hybrid Ensemble Learning Technique for Software Defect Prediction." International Journal of Modern Education and Computer Science 12, no. 1 (February 8, 2020): 1–10. http://dx.doi.org/10.5815/ijmecs.2020.01.01.
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Pandey, Hemakshi, Riya Goyal, Deepali Virmani, and Charu Gupta. "Ensem_SLDR: Classification of Cybercrime using Ensemble Learning Technique." International Journal of Computer Network and Information Security 14, no. 1 (February 8, 2021): 81–90. http://dx.doi.org/10.5815/ijcnis.2022.01.07.
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With the advancement of technology, cybercrimes are surging at an alarming rate as miscreants pour into the world's modern reliance on the virtual platform. Due to the accumulation of an enormous quantity of cybercrime data, there is huge potential to analyze and segregate the data with the help of Machine Learning. The focus of this research is to construct a model, Ensem_SLDR which can predict the relevant sections of IT Act 2000 from the compliant text/subjects with the aid of Natural Language Processing, Machine Learning, and Ensemble Learning methods. The objective of this paper is to implement a robust technique to categorize cybercrime into two sections, 66 and 67 of IT Act 2000 with high precision using ensemble learning technique. In the proposed methodology, Bag of Words approach is applied for performing feature engineering where these features are given as input to the hybrid model Ensem_SLDR. The proposed model is implemented with the help of model stacking, comprising Support Vector Machine (SVM), Logistic Regression, Decision Tree, and Random Forest and gave better performance by having 96.55 % accuracy, which is higher and reliable than the past models implemented using a single learning algorithm and some of the existing hybrid models. Ensemble learning techniques enhance model performance and robustness. This research is beneficial for cyber-crime cells in India, which have a repository of detailed information on cybercrime including complaints and investigations. Hence, there is a need for model and automation systems empowered by artificial intelligence technologies for the analysis of cybercrime and their classification of its sections.
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Al Duhayyim, Mesfer, Sidra Abbas, Abdullah Al Hejaili, Natalia Kryvinska, Ahmad Almadhor, and Uzma Ghulam Mohammad. "An Ensemble Machine Learning Technique for Stroke Prognosis." Computer Systems Science and Engineering 47, no. 1 (2023): 413–29. http://dx.doi.org/10.32604/csse.2023.037127.
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Chandra Jena, Prakash, Subhendu Kumar Pani, and Debahuti Mishra. "A novel approach to ensemble learning in distributed data mining." International Journal of Engineering & Technology 7, no. 3.3 (June 8, 2018): 233. http://dx.doi.org/10.14419/ijet.v7i2.33.14159.
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Several data mining techniques have been proposed to take out hidden information from databases. Data mining and knowledge extraction becomes challenging when data is massive, distributed and heterogeneous. Classification is an extensively applied task in data mining for prediction. Huge numbers of machine learning techniques have been developed for the purpose. Ensemble learning merges multiple base classifiers to improve the performance of individual classification algorithms. In particular, ensemble learning plays a significant role in distributed data mining. So, study of ensemble learning is crucial in order to apply it in real-world data mining problems. We propose a technique to construct ensemble of classifiers and study its performance using popular learning techniques on a range of publicly available datasets from biomedical domain.
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Dhanwanth, Batini, Bandi Vivek, M. Abirami, Shaik Mohammad Waseem, and Challapalli Manikantaa. "Forecasting Chronic Kidney Disease Using Ensemble Machine Learning Technique." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 5s (June 1, 2023): 336–44. http://dx.doi.org/10.17762/ijritcc.v11i5s.7035.
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India is a rapidly expanding nation on a global scale. Chronic kidney disease (CKD) is a prevalent health problem internationally, and advance perception of this disease can aid prevent its stream. This research proposes an ensemble learning technique that combines three different algorithms, Logistic Regression, Gradient Boosting and Random Forest for the prediction of CKD. The performance of each algorithm was judged based on Root Mean Square Error (RMSE) and Mean Square Error (MSE) as performance metrics, and the predictions of each algorithm were combined using an ensemble learning technique. The dataset used for the study contained data on 400 individuals with 24 different features, which was pre-processed by removing missing values and normalizing the data. The combined algorithm showed a better performance with an RMSE of 0.2111 and an MSE of 0.0446, compared to individual algorithms. The proposed ensemble learning technique can be utilized as a divining for advance perception of CKD. The outcomes of the work reveal the effectiveness of the technique and its potential for improving patient outcomes by preventing the progression of CKD. Additionally, the ensemble learning technique can be applied to other predictive tasks to improve performance, indicating its broader applicability.
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Sun, Xiao Wei, and Hong Bo Zhou. "Research on Applied Technology in Experiments with Three Boosting Algorithms." Advanced Materials Research 908 (March 2014): 513–16. http://dx.doi.org/10.4028/www.scientific.net/amr.908.513.
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Boosting algorithms are a means of building a strong ensemble classifier by aggregating a sequence of weak hypotheses. An ensemble consists of a set of independently trained classifiers whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. In this paper we use applied technology to built an ensemble using a voting methodology of Boosting-BAN and Boosting-MultiTAN ensembles with 10 sub-classifiers in each one. We performed a comparison with Boosting-BAN and Boosting-MultiTAN ensembles with 25 sub-classifiers on standard benchmark datasets and the proposed technique was the most accurate. These results argue that boosting algorithms deserve more attention in machine learning and data mining communities.
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Liu, Rencheng, Saqib Ali, Syed Fakhar Bilal, Zareen Sakhawat, Azhar Imran, Abdullah Almuhaimeed, Abdulkareem Alzahrani, and Guangmin Sun. "An Intelligent Hybrid Scheme for Customer Churn Prediction Integrating Clustering and Classification Algorithms." Applied Sciences 12, no. 18 (September 18, 2022): 9355. http://dx.doi.org/10.3390/app12189355.
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Nowadays, customer churn has been reflected as one of the main concerns in the processes of the telecom sector, as it affects the revenue directly. Telecom companies are looking to design novel methods to identify the potential customer to churn. Hence, it requires suitable systems to overcome the growing churn challenge. Recently, integrating different clustering and classification models to develop hybrid learners (ensembles) has gained wide acceptance. Ensembles are getting better approval in the domain of big data since they have supposedly achieved excellent predictions as compared to single classifiers. Therefore, in this study, we propose a customer churn prediction (CCP) based on ensemble system fully incorporating clustering and classification learning techniques. The proposed churn prediction model uses an ensemble of clustering and classification algorithms to improve CCP model performance. Initially, few clustering algorithms such as k-means, k-medoids, and Random are employed to test churn prediction datasets. Next, to enhance the results hybridization technique is applied using different ensemble algorithms to evaluate the performance of the proposed system. Above mentioned clustering algorithms integrated with different classifiers including Gradient Boosted Tree (GBT), Decision Tree (DT), Random Forest (RF), Deep Learning (DL), and Naive Bayes (NB) are evaluated on two standard telecom datasets which were acquired from Orange and Cell2Cell. The experimental result reveals that compared to the bagging ensemble technique, the stacking-based hybrid model (k-medoids-GBT-DT-DL) achieve the top accuracies of 96%, and 93.6% on the Orange and Cell2Cell dataset, respectively. The proposed method outperforms conventional state-of-the-art churn prediction algorithms.
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Shah, Shariq, Hossein Ghomeshi, Edlira Vakaj, Emmett Cooper, and Rasheed Mohammad. "An Ensemble-Learning-Based Technique for Bimodal Sentiment Analysis." Big Data and Cognitive Computing 7, no. 2 (April 30, 2023): 85. http://dx.doi.org/10.3390/bdcc7020085.
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Human communication is predominantly expressed through speech and writing, which are powerful mediums for conveying thoughts and opinions. Researchers have been studying the analysis of human sentiments for a long time, including the emerging area of bimodal sentiment analysis in natural language processing (NLP). Bimodal sentiment analysis has gained attention in various areas such as social opinion mining, healthcare, banking, and more. However, there is a limited amount of research on bimodal conversational sentiment analysis, which is challenging due to the complex nature of how humans express sentiment cues across different modalities. To address this gap in research, a comparison of multiple data modality models has been conducted on the widely used MELD dataset, which serves as a benchmark for sentiment analysis in the research community. The results show the effectiveness of combining acoustic and linguistic representations using a proposed neural-network-based ensemble learning technique over six transformer and deep-learning-based models, achieving state-of-the-art accuracy.
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Vacchetti, Bartolomeo, and Tania Cerquitelli. "Cinematographic Shot Classification with Deep Ensemble Learning." Electronics 11, no. 10 (May 13, 2022): 1570. http://dx.doi.org/10.3390/electronics11101570.
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Cinematographic shot classification assigns a category to each shot either on the basis of the field size or on the movement performed by the camera. In this work, we focus on the camera field of view, which is determined by the portion of the subject and of the environment shown in the field of view of the camera. The automation of this task can help freelancers and studios belonging to the visual creative field in their daily activities. In our study, we took into account eight classes of film shots: long shot, medium shot, full figure, american shot, half figure, half torso, close up and extreme close up. The cinematographic shot classification is a complex task, so we combined state-of-the-art techniques to deal with it. Specifically, we finetuned three separated VGG-16 models and combined their predictions in order to obtain better performances by exploiting the stacking learning technique. Experimental results demonstrate the effectiveness of the proposed approach in performing the classification task with good accuracy. Our method was able to achieve 77% accuracy without relying on data augmentation techniques. We also evaluated our approach in terms of f1 score, precision, and recall and we showed confusion matrices to show that most of our misclassified samples belonged to a neighboring class.
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Cui, Su, Yiliang Han, Yifei Duan, Yu Li, Shuaishuai Zhu, and Chaoyue Song. "A Two-Stage Voting-Boosting Technique for Ensemble Learning in Social Network Sentiment Classification." Entropy 25, no. 4 (March 24, 2023): 555. http://dx.doi.org/10.3390/e25040555.
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In recent years, social network sentiment classification has been extensively researched and applied in various fields, such as opinion monitoring, market analysis, and commodity feedback. The ensemble approach has achieved remarkable results in sentiment classification tasks due to its superior performance. The primary reason behind the success of ensemble methods is the enhanced diversity of the base classifiers. The boosting method employs a sequential ensemble structure to construct diverse data while also utilizing erroneous data by assigning higher weights to misclassified samples in the next training round. However, this method tends to use a sequential ensemble structure, resulting in a long computation time. Conversely, the voting method employs a concurrent ensemble structure to reduce computation time but neglects the utilization of erroneous data. To address this issue, this study combines the advantages of voting and boosting methods and proposes a new two-stage voting boosting (2SVB) concurrent ensemble learning method for social network sentiment classification. This novel method not only establishes a concurrent ensemble framework to decrease computation time but also optimizes the utilization of erroneous data and enhances ensemble performance. To optimize the utilization of erroneous data, a two-stage training approach is implemented. Stage-1 training is performed on the datasets by employing a 3-fold cross-segmentation approach. Stage-2 training is carried out on datasets that have been augmented with the erroneous data predicted by stage 1. To augment the diversity of base classifiers, the training stage employs five pre-trained deep learning (PDL) models with heterogeneous pre-training frameworks as base classifiers. To reduce the computation time, a two-stage concurrent ensemble framework was established. The experimental results demonstrate that the proposed method achieves an F1 score of 0.8942 on the coronavirus tweet sentiment dataset, surpassing other comparable ensemble methods.
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Troć, Maciej, and Olgierd Unold. "Self-adaptation of parameters in a learning classifier system ensemble machine." International Journal of Applied Mathematics and Computer Science 20, no. 1 (March 1, 2010): 157–74. http://dx.doi.org/10.2478/v10006-010-0012-8.
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Self-adaptation of parameters in a learning classifier system ensemble machineSelf-adaptation is a key feature of evolutionary algorithms (EAs). Although EAs have been used successfully to solve a wide variety of problems, the performance of this technique depends heavily on the selection of the EA parameters. Moreover, the process of setting such parameters is considered a time-consuming task. Several research works have tried to deal with this problem; however, the construction of algorithms letting the parameters adapt themselves to the problem is a critical and open problem of EAs. This work proposes a novel ensemble machine learning method that is able to learn rules, solve problems in a parallel way and adapt parameters used by its components. A self-adaptive ensemble machine consists of simultaneously working extended classifier systems (XCSs). The proposed ensemble machine may be treated as a meta classifier system. A new self-adaptive XCS-based ensemble machine was compared with two other XCS-based ensembles in relation to one-step binary problems: Multiplexer, One Counts, Hidden Parity, and randomly generated Boolean functions, in a noisy version as well. Results of the experiments have shown the ability of the model to adapt the mutation rate and the tournament size. The results are analyzed in detail.
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Chandrasekar, Jayakumar, Surendar Madhawa, and J. Sangeetha. "Data-driven disruption prediction in GOLEM Tokamak using ensemble classifiers." Journal of Intelligent & Fuzzy Systems 39, no. 6 (December 4, 2020): 8365–76. http://dx.doi.org/10.3233/jifs-189155.
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A robust disruption prediction system is mandatory in a Tokamak control system as the disruption can cause malfunctioning of the plasma-facing components and impair irrecoverable structural damage to the vessel. To mitigate the disruption, in this article, a data-driven based disruption predictor is developed using an ensemble technique. The ensemble algorithm classifies disruptive and non-disruptive discharges in the GOLEM Tokamak system. Ensemble classifiers combine the predictive capacity of several weak learners to produce a single predictive model and are utilized both in supervised and unsupervised learning. The resulting final model reduces the bias, minimizes variance and is unlikely to over-fit when compared to the individual model from a single algorithm. In this paper, popular ensemble techniques such as Bagging, Boosting, Voting, and Stacking are employed on the time-series Tokamak dataset, which consists of 117 normal and 70 disruptive shots. Stacking ensemble with REPTree (Reduced Error Pruning Tree) as a base learner and Multi-response Linear Regression as meta learner produced better results in comparison to other ensembles. A comparison with the widely employed stand-alone machine learning algorithms and ensemble algorithms are illustrated. The results show the excellent performance of the Stacking model with an F1 score of 0.973. The developed predictive model would be capable of warning the human operator with feedback about the feature(s) causing the disruption.
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Rhmann, Wasiur. "An Ensemble of Hybrid Search-Based Algorithms for Software Effort Prediction." International Journal of Software Science and Computational Intelligence 13, no. 3 (July 2021): 28–37. http://dx.doi.org/10.4018/ijssci.2021070103.
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Software organizations rely on the estimation of efforts required for the development of software to negotiate customers and plan the schedule of the project. Proper estimation of efforts reduces the chances of project failures. Historical data of projects have been used to predict the effort required for software development. In recent years, various ensemble of machine learning techniques have been used to predict software effort. In the present work, a novel ensemble technique of hybrid search-based algorithms (EHSBA) is used for software effort estimation. Four HSBAs—fuzzy and random sets-based modeling (FRSBM-R), symbolic fuzzy learning based on genetic programming (GFS-GP-R), symbolic fuzzy learning based on genetic programming grammar operators and simulated annealing (GFS_GSP_R), and least mean squares linear regression (LinearLMS_R)—are used to create an ensemble (EHSBA). The EHSBA is compared with machine learning-based ensemble bagging, vote, and stacking on datasets obtained from PROMISE repository. Obtained results reported that EHSBA outperformed all other techniques.
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Li, Xingjian, Haoyi Xiong, Zeyu Chen, Jun Huan, Cheng-Zhong Xu, and Dejing Dou. "“In-Network Ensemble”: Deep Ensemble Learning with Diversified Knowledge Distillation." ACM Transactions on Intelligent Systems and Technology 12, no. 5 (October 31, 2021): 1–19. http://dx.doi.org/10.1145/3473464.
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Ensemble learning is a widely used technique to train deep convolutional neural networks (CNNs) for improved robustness and accuracy. While existing algorithms usually first train multiple diversified networks and then assemble these networks as an aggregated classifier, we propose a novel learning paradigm, namely, “In-Network Ensemble” ( INE ) that incorporates the diversity of multiple models through training a SINGLE deep neural network. Specifically, INE segments the outputs of the CNN into multiple independent classifiers, where each classifier is further fine-tuned with better accuracy through a so-called diversified knowledge distillation process . We then aggregate the fine-tuned independent classifiers using an Averaging-and-Softmax operator to obtain the final ensemble classifier. Note that, in the supervised learning settings, INE starts the CNN training from random, while, under the transfer learning settings, it also could start with a pre-trained model to incorporate the knowledge learned from additional datasets. Extensive experiments have been done using eight large-scale real-world datasets, including CIFAR, ImageNet, and Stanford Cars, among others, as well as common deep network architectures such as VGG, ResNet, and Wide ResNet. We have evaluated the method under two tasks: supervised learning and transfer learning. The results show that INE outperforms the state-of-the-art algorithms for deep ensemble learning with improved accuracy.
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Adamu, Yusuf Aliyu. "MALARIA PREDICTION MODEL USING ADVANCED ENSEMBLE MACHINE LEARNING TECHNIQUES." Journal of Medical pharmaceutical and allied sciences 10, no. 6 (December 15, 2021): 3794–801. http://dx.doi.org/10.22270/jmpas.v10i6.1701.
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Malaria is a life-threatening disease that leads to death globally, its early prediction is necessary for preventing the rapid transmission. In this work, an enhanced ensemble learning approach for predicting malaria outbreaks is suggested. Using a mean-based splitting strategy, the dataset is randomly partitioned into smaller groups. The splits are then modelled using a classification and regression tree, and an accuracy-based weighted aging classifier ensemble is used to construct a homogenous ensemble from the several Classification and Regression Tree models. This approach ensures higher performance is achieved. Seven different Algorithms were tested and one ensemble method is used which combines all the seven classifiers together and finally, the accuracy, precision, and sensitivity achieved for the proposed method is 93%, 92%, and 100% respectively, which outperformed better than machine learning classifiers and ensemble method used in this research. The correlation between the variables used is established and how each factor contributes to the malaria incidence. The result indicates that malaria outbreaks can be predicted successfully using the suggested technique.
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Ferano, Francisco Calvin Arnel, Amalia Zahra, and Gede Putra Kusuma. "Stacking ensemble learning for optical music recognition." Bulletin of Electrical Engineering and Informatics 12, no. 5 (October 1, 2023): 3095–104. http://dx.doi.org/10.11591/eei.v12i5.5129.
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The development of music culture has resulted in a problem called optical music recognition (OMR). OMR is a task in computer vision that explores the algorithms and models to recognize musical notation. This study proposed the stacking ensemble learning model to complete the OMR task using the common western musical notation (CWMN) musical notation. The ensemble learning model used four deep convolutional neural networks (DCNNs) models, namely ResNeXt50, Inception-V3, RegNetY-400MF, and EfficientNet-V2-S as the base classifier. This study also analysed the most appropriate technique to be used as the ensemble learning model’s meta-classifier. Therefore, several machine learning techniques are determined to be evaluated, namely support vector machine (SVM), logistic regression (LR), random forest (RF), K-nearest neighbor (KNN), decision tree (DT), and Naïve Bayes (NB). Six publicly available OMR datasets are combined, down sampled, and used to test the proposed model. The dataset consists of the HOMUS_V2, Rebelo1, Rebelo2, Fornes, OpenOMR, and PrintedMusicSymbols datasets. The proposed ensemble learning model managed to outperform the model built in the previous study and succeeded in achieving outstanding accuracy and F1-scores with the best value of 97.51% and 97.52%, respectively; both of which were achieved by the LR meta-classifier.
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Christianah, Abikoye Oluwakemi, Benjamin Aruwa Gyunka, and Akande Noah Oluwatobi. "Optimizing Android Malware Detection Via Ensemble Learning." International Journal of Interactive Mobile Technologies (iJIM) 14, no. 09 (June 17, 2020): 61. http://dx.doi.org/10.3991/ijim.v14i09.11548.
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<p>Android operating system has become very popular, with the highest market share, amongst all other mobile operating systems due to its open source nature and users friendliness. This has brought about an uncontrolled rise in malicious applications targeting the Android platform. Emerging trends of Android malware are employing highly sophisticated detection and analysis avoidance techniques such that the traditional signature-based detection methods have become less potent in their ability to detect new and unknown malware. Alternative approaches, such as the Machine learning techniques have taken the lead for timely zero-day anomaly detections. The study aimed at developing an optimized Android malware detection model using ensemble learning technique. Random Forest, Support Vector Machine, and k-Nearest Neighbours were used to develop three distinct base models and their predictive results were further combined using Majority Vote combination function to produce an ensemble model. Reverse engineering procedure was employed to extract static features from large repository of malware samples and benign applications. WEKA 3.8.2 data mining suite was used to perform all the learning experiments. The results showed that Random Forest had a true positive rate of 97.9%, a false positive rate of 1.9% and was able to correctly classify instances with 98%, making it a strong base model. The ensemble model had a true positive rate of 98.1%, false positive rate of 1.8% and was able to correctly classify instances with 98.16%. The finding shows that, although the base learners had good detection results, the ensemble learner produced a better optimized detection model compared with the performances of those of the base learners.</p>
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Munsarif, Muhammad, Muhammad Sam’an, and Safuan Safuan. "Peer to peer lending risk analysis based on embedded technique and stacking ensemble learning." Bulletin of Electrical Engineering and Informatics 11, no. 6 (December 1, 2022): 3483–89. http://dx.doi.org/10.11591/eei.v11i6.3927.
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Peer to peer lending is famous for easy and fast loans from complicated traditional lending institutions. Therefore, big data and machine learning are needed for credit risk analysis, especially for potential defaulters. However, data imbalance and high computation have a terrible effect on machine learning prediction performance. This paper proposes a stacking ensemble learning with features selection based on embedded techniques (gradient boosted trees (GBDT), random forest (RF), adaptive boosting (AdaBoost), extra gradient boosting (XGBoost), light gradient boosting machine (LGBM), and decision tree (DT)) to predict the credit risk of individual borrowers on peer to peer (P2P) lending. The stacking ensemble model is created from a stack of meta-learners used in feature selection. The feature selection+ stacking model produces an average of 94.54% accuracy and 69.10 s execution time. RF meta-learner+Stacking ensemble is the best classification model, and the LGBM meta-learner+stacking ensemble is the fastest execution time. Based on experimental results, this paper showed that the credit risk prediction for P2P lending could be improved using the stacking ensemble model in addition to proper feature selection.
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Hamori, Hitoshi, and Shigeyuki Hamori. "Does Ensemble Learning Always Lead to Better Forecasts?" Applied Economics and Finance 7, no. 2 (February 12, 2020): 51. http://dx.doi.org/10.11114/aef.v7i2.4716.
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Ensemble learning is a common machine learning technique applied to business and economic analysis in which several classifiers are combined using majority voting for better forecasts as compared to those of individual classifier. This study presents a counterexample, which demonstrates that ensemble learning leads to worse classifications than those from individual classifiers, using two events and three classifiers. If there is an outstanding classifier, we should follow its forecast instead of using ensemble learning.
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Mahajan, Palak, Shahadat Uddin, Farshid Hajati, and Mohammad Ali Moni. "Ensemble Learning for Disease Prediction: A Review." Healthcare 11, no. 12 (June 20, 2023): 1808. http://dx.doi.org/10.3390/healthcare11121808.
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Machine learning models are used to create and enhance various disease prediction frameworks. Ensemble learning is a machine learning technique that combines multiple classifiers to improve performance by making more accurate predictions than a single classifier. Although numerous studies have employed ensemble approaches for disease prediction, there is a lack of thorough assessment of commonly used ensemble approaches against highly researched diseases. Consequently, this study aims to identify significant trends in the performance accuracies of ensemble techniques (i.e., bagging, boosting, stacking, and voting) against five hugely researched diseases (i.e., diabetes, skin disease, kidney disease, liver disease, and heart conditions). Using a well-defined search strategy, we first identified 45 articles from the current literature that applied two or more of the four ensemble approaches to any of these five diseases and were published in 2016–2023. Although stacking has been used the fewest number of times (23) compared with bagging (41) and boosting (37), it showed the most accurate performance the most times (19 out of 23). The voting approach is the second-best ensemble approach, as revealed in this review. Stacking always revealed the most accurate performance in the reviewed articles for skin disease and diabetes. Bagging demonstrated the best performance for kidney disease (five out of six times) and boosting for liver and diabetes (four out of six times). The results show that stacking has demonstrated greater accuracy in disease prediction than the other three candidate algorithms. Our study also demonstrates variability in the perceived performance of different ensemble approaches against frequently used disease datasets. The findings of this work will assist researchers in better understanding current trends and hotspots in disease prediction models that employ ensemble learning, as well as in determining a more suitable ensemble model for predictive disease analytics. This article also discusses variability in the perceived performance of different ensemble approaches against frequently used disease datasets.
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Sarkar, Nipa, and Asha Rani Borah. "Predicting ESRD Risk via Supervised and Ensemble Machine Learning Technique." International Journal of Research in Advent Technology 7, no. 4 (April 10, 2019): 173–77. http://dx.doi.org/10.32622/ijrat.74201970.
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Lee, Yen-Hsien, Paul Jen-Hwa Hu, Tsang-Hsiang Cheng, Te-Chia Huang, and Wei-Yao Chuang. "A preclustering-based ensemble learning technique for acute appendicitis diagnoses." Artificial Intelligence in Medicine 58, no. 2 (June 2013): 115–24. http://dx.doi.org/10.1016/j.artmed.2013.03.007.
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Alruily, Meshrif, Sameh Abd El-Ghany, Ayman Mohamed Mostafa, Mohamed Ezz, and A. A. Abd El-Aziz. "A-Tuning Ensemble Machine Learning Technique for Cerebral Stroke Prediction." Applied Sciences 13, no. 8 (April 18, 2023): 5047. http://dx.doi.org/10.3390/app13085047.
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A cerebral stroke is a medical problem that occurs when the blood flowing to a section of the brain is suddenly cut off, causing damage to the brain. Brain cells gradually die because of interruptions in blood supply and other nutrients to the brain, resulting in disabilities, depending on the affected region. Early recognition and detection of symptoms can aid in the rapid treatment of strokes and result in better health by reducing the severity of a stroke episode. In this paper, the Random Forest (RF), Extreme Gradient Boosting (XGBoost), and light gradient-boosting machine (LightGBM) were used as machine learning (ML) algorithms for predicting the likelihood of a cerebral stroke by applying an open-access stroke prediction dataset. The stroke prediction dataset was pre-processed by handling missing values using the KNN imputer technique, eliminating outliers, applying the one-hot encoding method, and normalizing the features with different ranges of values. After data splitting, synthetic minority oversampling (SMO) was applied to balance the stroke samples and no-stroke classes. Furthermore, to fine-tune the hyper-parameters of the ML algorithm, we employed a random search technique that could achieve the best parameter values. After applying the tuning process, we stacked the parameters to a tuning ensemble RXLM that was analyzed and compared with traditional classifiers. The performance metrics after tuning the hyper-parameters achieved promising results with all ML algorithms.
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Krasnopolsky, Vladimir M., and Ying Lin. "A Neural Network Nonlinear Multimodel Ensemble to Improve Precipitation Forecasts over Continental US." Advances in Meteorology 2012 (2012): 1–11. http://dx.doi.org/10.1155/2012/649450.
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A novel multimodel ensemble approach based on learning from data using the neural network (NN) technique is formulated and applied for improving 24-hour precipitation forecasts over the continental US. The developed nonlinear approach allowed us to account for nonlinear correlation between ensemble members and to produce “optimal” forecast represented by a nonlinear NN ensemble mean. The NN approach is compared with the conservative multi-model ensemble, with multiple linear regression ensemble approaches, and with results obtained by human forecasters. The NN multi-model ensemble improves upon conservative multi-model ensemble and multiple linear regression ensemble, it (1) significantly reduces high bias at low precipitation level, (2) significantly reduces low bias at high precipitation level, and (3) sharpens features making them closer to the observed ones. The NN multi-model ensemble performs at least as well as human forecasters supplied with the same information. The developed approach is a generic approach that can be applied to other multi-model ensemble fields as well as to single model ensembles.
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Verma, Pratibha, Vineet Kumar Awasthi, and Sanat Kumar Sahu. "A Novel Design of Classification of Coronary Artery Disease Using Deep Learning and Data Mining Algorithms." Revue d'Intelligence Artificielle 35, no. 3 (June 30, 2021): 209–15. http://dx.doi.org/10.18280/ria.350304.
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Data mining techniques are included with Ensemble learning and deep learning for the classification. The methods used for classification are, Single C5.0 Tree (C5.0), Classification and Regression Tree (CART), kernel-based Support Vector Machine (SVM) with linear kernel, ensemble (CART, SVM, C5.0), Neural Network-based Fit single-hidden-layer neural network (NN), Neural Networks with Principal Component Analysis (PCA-NN), deep learning-based H2OBinomialModel-Deeplearning (HBM-DNN) and Enhanced H2OBinomialModel-Deeplearning (EHBM-DNN). In this study, experiments were conducted on pre-processed datasets using R programming and 10-fold cross-validation technique. The findings show that the ensemble model (CART, SVM and C5.0) and EHBM-DNN are more accurate for classification, compared with other methods.
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Devi, Debashree, Suyel Namasudra, and Seifedine Kadry. "A Boosting-Aided Adaptive Cluster-Based Undersampling Approach for Treatment of Class Imbalance Problem." International Journal of Data Warehousing and Mining 16, no. 3 (July 2020): 60–86. http://dx.doi.org/10.4018/ijdwm.2020070104.
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The subject of a class imbalance is a well-investigated topic which addresses performance degradation of standard learning models due to uneven distribution of classes in a dataspace. Cluster-based undersampling is a popular solution in the domain which offers to eliminate majority class instances from a definite number of clusters to balance the training data. However, distance-based elimination of instances often got affected by the underlying data distribution. Recently, ensemble learning techniques have emerged as effective solution due to its weighted learning principle of rare instances. In this article, a boosting aided adaptive cluster-based undersampling technique is proposed to facilitate elimination of learning- insignificant majority class instances from the clusters, detected through AdaBoost ensemble learning model. The proposed work is validated with seven existing cluster based undersampling techniques for six binary datasets and three classification models. The experimental results have established the effectives of the proposed technique than the existing methods.
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Salunkhe, Uma R., and Suresh N. Mali. "Security Enrichment in Intrusion Detection System Using Classifier Ensemble." Journal of Electrical and Computer Engineering 2017 (2017): 1–6. http://dx.doi.org/10.1155/2017/1794849.
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In the era of Internet and with increasing number of people as its end users, a large number of attack categories are introduced daily. Hence, effective detection of various attacks with the help of Intrusion Detection Systems is an emerging trend in research these days. Existing studies show effectiveness of machine learning approaches in handling Intrusion Detection Systems. In this work, we aim to enhance detection rate of Intrusion Detection System by using machine learning technique. We propose a novel classifier ensemble based IDS that is constructed using hybrid approach which combines data level and feature level approach. Classifier ensembles combine the opinions of different experts and improve the intrusion detection rate. Experimental results show the improved detection rates of our system compared to reference technique.
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Tsai, Chih-Fong, and Chihli Hung. "Modeling credit scoring using neural network ensembles." Kybernetes 43, no. 7 (July 29, 2014): 1114–23. http://dx.doi.org/10.1108/k-01-2014-0016.
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Purpose – Credit scoring is important for financial institutions in order to accurately predict the likelihood of business failure. Related studies have shown that machine learning techniques, such as neural networks, outperform many statistical approaches to solving this type of problem, and advanced machine learning techniques, such as classifier ensembles and hybrid classifiers, provide better prediction performance than single machine learning based classification techniques. However, it is not known which type of advanced classification technique performs better in terms of financial distress prediction. The paper aims to discuss these issues. Design/methodology/approach – This paper compares neural network ensembles and hybrid neural networks over three benchmarking credit scoring related data sets, which are Australian, German, and Japanese data sets. Findings – The experimental results show that hybrid neural networks and neural network ensembles outperform the single neural network. Although hybrid neural networks perform slightly better than neural network ensembles in terms of predication accuracy and errors with two of the data sets, there is no significant difference between the two types of prediction models. Originality/value – The originality of this paper is in comparing two types of advanced classification techniques, i.e. hybrid and ensemble learning techniques, in terms of financial distress prediction.
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Tang, Ling, Wei Dai, Lean Yu, and Shouyang Wang. "A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting." International Journal of Information Technology & Decision Making 14, no. 01 (January 2015): 141–69. http://dx.doi.org/10.1142/s0219622015400015.
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To enhance the prediction accuracy for crude oil price, a novel ensemble learning paradigm coupling complementary ensemble empirical mode decomposition (CEEMD) and extended extreme learning machine (EELM) is proposed. This novel method is actually an improved model under the effective "decomposition and ensemble" framework, especially for nonlinear, complex, and irregular data. In this proposed method, CEEMD, a current extension from the competitive decomposition family of empirical mode decomposition (EMD), is first applied to divide the original data (i.e., difficult task) into a number of components (i.e., relatively easy subtasks). Then, EELM, a recently developed, powerful, fast and stable intelligent learning technique, is implemented to predict all extracted components individually. Finally, these predicted results are aggregated into an ensemble result as the final prediction using simple addition ensemble method. With the crude oil spot prices of WTI and Brent as sample data, the empirical results demonstrate that the novel CEEMD-based EELM ensemble model statistically outperforms all listed benchmarks (including typical forecasting techniques and similar ensemble models with other decomposition and ensemble tools) in prediction accuracy. The results also indicate that the novel model can be used as a promising forecasting tool for complicated time series data with high volatility and irregularity.
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Ali, Abdullah Marish, Fuad A. Ghaleb, Bander Ali Saleh Al-Rimy, Fawaz Jaber Alsolami, and Asif Irshad Khan. "Deep Ensemble Fake News Detection Model Using Sequential Deep Learning Technique." Sensors 22, no. 18 (September 15, 2022): 6970. http://dx.doi.org/10.3390/s22186970.
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Recently, fake news has been widely spread through the Internet due to the increased use of social media for communication. Fake news has become a significant concern due to its harmful impact on individual attitudes and the community’s behavior. Researchers and social media service providers have commonly utilized artificial intelligence techniques in the recent few years to rein in fake news propagation. However, fake news detection is challenging due to the use of political language and the high linguistic similarities between real and fake news. In addition, most news sentences are short, therefore finding valuable representative features that machine learning classifiers can use to distinguish between fake and authentic news is difficult because both false and legitimate news have comparable language traits. Existing fake news solutions suffer from low detection performance due to improper representation and model design. This study aims at improving the detection accuracy by proposing a deep ensemble fake news detection model using the sequential deep learning technique. The proposed model was constructed in three phases. In the first phase, features were extracted from news contents, preprocessed using natural language processing techniques, enriched using n-gram, and represented using the term frequency–inverse term frequency technique. In the second phase, an ensemble model based on deep learning was constructed as follows. Multiple binary classifiers were trained using sequential deep learning networks to extract the representative hidden features that could accurately classify news types. In the third phase, a multi-class classifier was constructed based on multilayer perceptron (MLP) and trained using the features extracted from the aggregated outputs of the deep learning-based binary classifiers for final classification. The two popular and well-known datasets (LIAR and ISOT) were used with different classifiers to benchmark the proposed model. Compared with the state-of-the-art models, which use deep contextualized representation with convolutional neural network (CNN), the proposed model shows significant improvements (2.41%) in the overall performance in terms of the F1score for the LIAR dataset, which is more challenging than other datasets. Meanwhile, the proposed model achieves 100% accuracy with ISOT. The study demonstrates that traditional features extracted from news content with proper model design outperform the existing models that were constructed based on text embedding techniques.
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Namoun, Abdallah, Burhan Rashid Hussein, Ali Tufail, Ahmed Alrehaili, Toqeer Ali Syed, and Oussama BenRhouma. "An Ensemble Learning Based Classification Approach for the Prediction of Household Solid Waste Generation." Sensors 22, no. 9 (May 5, 2022): 3506. http://dx.doi.org/10.3390/s22093506.
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With the increase in urbanization and smart cities initiatives, the management of waste generation has become a fundamental task. Recent studies have started applying machine learning techniques to prognosticate solid waste generation to assist authorities in the efficient planning of waste management processes, including collection, sorting, disposal, and recycling. However, identifying the best machine learning model to predict solid waste generation is a challenging endeavor, especially in view of the limited datasets and lack of important predictive features. In this research, we developed an ensemble learning technique that combines the advantages of (1) a hyperparameter optimization and (2) a meta regressor model to accurately predict the weekly waste generation of households within urban cities. The hyperparameter optimization of the models is achieved using the Optuna algorithm, while the outputs of the optimized single machine learning models are used to train the meta linear regressor. The ensemble model consists of an optimized mixture of machine learning models with different learning strategies. The proposed ensemble method achieved an R2 score of 0.8 and a mean percentage error of 0.26, outperforming the existing state-of-the-art approaches, including SARIMA, NARX, LightGBM, KNN, SVR, ETS, RF, XGBoosting, and ANN, in predicting future waste generation. Not only did our model outperform the optimized single machine learning models, but it also surpassed the average ensemble results of the machine learning models. Our findings suggest that using the proposed ensemble learning technique, even in the case of a feature-limited dataset, can significantly boost the model performance in predicting future household waste generation compared to individual learners. Moreover, the practical implications for the research community and respective city authorities are discussed.
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Li, Kai, and Hong Tao Gao. "A Subgraph-Based Selective Classifier Ensemble Algorithm." Advanced Materials Research 219-220 (March 2011): 261–64. http://dx.doi.org/10.4028/www.scientific.net/amr.219-220.261.
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To improve the generalization performance for ensemble learning, a subgraph based selective classifier ensemble algorithm is presented. Firstly, a set of classifiers are generated by bootstrap sampling technique and support vector machine learning algorithm. And a complete undirected graph is constructed whose vertex is classifier and weight of edge between a pair of classifiers is diversity values. Secondly, by searching technique to find an edge with minimum weight and to calculate similarity values about two vertexes which is related to the edge, vertex with smaller similarity value is removed. According to this method, a subgraph is obtained. Finally, we choose vertexes of subgraph, i.e. classifiers, as ensemble members. Experiments show that presented method outperforms the traditional ensemble learning methods in classification accuracy.
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Srınıvasa Rao, B. "A New Ensenble Learning based Optimal Prediction Model for Cardiovascular Diseases." E3S Web of Conferences 309 (2021): 01007. http://dx.doi.org/10.1051/e3sconf/202130901007.
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The present paperreports an optimal machine learning model for an effective prediction of cardiovascular diseases that uses the ensemble learning technique. The present research work gives an insight about the coherent way of combining Naive Bayes and Random Forest algorithm using ensemble technique. It also discusses how the present model is different from other traditional approaches. The present experimental results manifest that the present optimal machine learning model is more efficient than the other models.
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Hashim, Dhurgham Kadhim, and Lamia Abed Noor Muhammed. "Performance of K-means algorithm based an ensemble learning." Bulletin of Electrical Engineering and Informatics 11, no. 1 (February 1, 2022): 575–80. http://dx.doi.org/10.11591/eei.v11i1.3550.
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K-means is an iterative algorithm used with clustering task. It has more characteristics such as simplicity. In the same time, it suffers from some of drawbacks, sensitivity to initial centroid values that may produce bad results, they are based on the initial centroids of clusters that would be selected randomly. More suggestions have been given in order to overcome this problem. Ensemble learning is a method used in clustering; multiple runs are executed that produce different results for the same data set. Then the final results are driven. According to this hypothesis, more ensemble learning techniques have been suggested to deal with the clustering problem. One of these techniques is "Three ways method". However, in this paper, three ways method as an ensemble technique would be suggested to be merged with k-mean algorithm in order to improve its performance and reduce the impact of initial centroids on results. Then it was compared with traditional k-means results through practical work that was executed using popular data set. The evaluation of the hypothesis was done through computing related metrics.
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Liu, Kun-Hong, Muchenxuan Tong, Shu-Tong Xie, and Vincent To Yee Ng. "Genetic Programming Based Ensemble System for Microarray Data Classification." Computational and Mathematical Methods in Medicine 2015 (2015): 1–11. http://dx.doi.org/10.1155/2015/193406.
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Recently, more and more machine learning techniques have been applied to microarray data analysis. The aim of this study is to propose a genetic programming (GP) based new ensemble system (named GPES), which can be used to effectively classify different types of cancers. Decision trees are deployed as base classifiers in this ensemble framework with three operators: Min, Max, and Average. Each individual of the GP is an ensemble system, and they become more and more accurate in the evolutionary process. The feature selection technique and balanced subsampling technique are applied to increase the diversity in each ensemble system. The final ensemble committee is selected by a forward search algorithm, which is shown to be capable of fitting data automatically. The performance of GPES is evaluated using five binary class and six multiclass microarray datasets, and results show that the algorithm can achieve better results in most cases compared with some other ensemble systems. By using elaborate base classifiers or applying other sampling techniques, the performance of GPES may be further improved.
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Ahmed, Kanwal, Muhammad Imran Nadeem, Dun Li, Zhiyun Zheng, Nouf Al-Kahtani, Hend Khalid Alkahtani, Samih M. Mostafa, and Orken Mamyrbayev. "Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis." Symmetry 15, no. 3 (March 3, 2023): 645. http://dx.doi.org/10.3390/sym15030645.
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The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.
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Ali, Muhammad Danish, Adnan Saleem, Hubaib Elahi, Muhammad Amir Khan, Muhammad Ijaz Khan, Muhammad Mateen Yaqoob, Umar Farooq Khattak, and Amal Al-Rasheed. "Breast Cancer Classification through Meta-Learning Ensemble Technique Using Convolution Neural Networks." Diagnostics 13, no. 13 (June 30, 2023): 2242. http://dx.doi.org/10.3390/diagnostics13132242.
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This study aims to develop an efficient and accurate breast cancer classification model using meta-learning approaches and multiple convolutional neural networks. This Breast Ultrasound Images (BUSI) dataset contains various types of breast lesions. The goal is to classify these lesions as benign or malignant, which is crucial for the early detection and treatment of breast cancer. The problem is that traditional machine learning and deep learning approaches often fail to accurately classify these images due to their complex and diverse nature. In this research, to address this problem, the proposed model used several advanced techniques, including meta-learning ensemble technique, transfer learning, and data augmentation. Meta-learning will optimize the model’s learning process, allowing it to adapt to new and unseen datasets quickly. Transfer learning will leverage the pre-trained models such as Inception, ResNet50, and DenseNet121 to enhance the model’s feature extraction ability. Data augmentation techniques will be applied to artificially generate new training images, increasing the size and diversity of the dataset. Meta ensemble learning techniques will combine the outputs of multiple CNNs, improving the model’s classification accuracy. The proposed work will be investigated by pre-processing the BUSI dataset first, then training and evaluating multiple CNNs using different architectures and pre-trained models. Then, a meta-learning algorithm will be applied to optimize the learning process, and ensemble learning will be used to combine the outputs of multiple CNN. Additionally, the evaluation results indicate that the model is highly effective with high accuracy. Finally, the proposed model’s performance will be compared with state-of-the-art approaches in other existing systems’ accuracy, precision, recall, and F1 score.
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Shamsuddin, Siti Nurasyikin, Noriszura Ismail, and R. Nur-Firyal. "Life Insurance Prediction and Its Sustainability Using Machine Learning Approach." Sustainability 15, no. 13 (July 7, 2023): 10737. http://dx.doi.org/10.3390/su151310737.
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Owning life insurance coverage that is not enough to pay for the expenses is called underinsurance, and it has been found to have a significant influence on the sustainability and financial health of families. However, insurance companies need to have a good profile of potential policyholders. Customer profiling has become one of the essential marketing strategies for any sustainable business, such as the insurance market, to identify potential life insurance purchasers. One well-known method of carrying out customer profiling and segmenting is machine learning. Hence, this study aims to provide a helpful framework for predicting potential life insurance policyholders using a data mining approach with different sampling methods and to lead to a transition to sustainable life insurance industry development. Various samplings, such as the Synthetic Minority Over-sampling Technique, Randomly Under-Sampling, and ensemble (bagging and boosting) techniques, are proposed to handle the imbalanced dataset. The result reveals that the decision tree is the best performer according to ROC and, according to balanced accuracy, F1 score, and GM comparison, Naïve Bayes seems to be the best performer. It is also found that ensemble models do not guarantee high performance in this imbalanced dataset. However, the ensembled and sampling method plays a significant role in overcoming the imbalanced problem.
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Tama, Bayu Adhi, and Marco Comuzzi. "Leveraging a Heterogeneous Ensemble Learning for Outcome-Based Predictive Monitoring Using Business Process Event Logs." Electronics 11, no. 16 (August 15, 2022): 2548. http://dx.doi.org/10.3390/electronics11162548.
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Outcome-based predictive process monitoring concerns predicting the outcome of a running process case using historical events stored as so-called process event logs. This prediction problem has been approached using different learning models in the literature. Ensemble learners have been shown to be particularly effective in outcome-based business process predictive monitoring, even when compared with learners exploiting complex deep learning architectures. However, the ensemble learners that have been used in the literature rely on weak base learners, such as decision trees. In this article, an advanced stacking ensemble technique for outcome-based predictive monitoring is introduced. The proposed stacking ensemble employs strong learners as base classifiers, i.e., other ensembles. More specifically, we consider stacking of random forests, extreme gradient boosting machines, and gradient boosting machines to train a process outcome prediction model. We evaluate the proposed approach using publicly available event logs. The results show that the proposed model is a promising approach for the outcome-based prediction task. We extensively compare the performance differences among the proposed methods and the base strong learners, using also statistical tests to prove the generalizability of the results obtained.