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Auswahl der wissenschaftlichen Literatur zum Thema „Online ensemble regression“
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Zeitschriftenartikel zum Thema "Online ensemble regression"
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Liu, Yang, Bo He, Diya Dong, Yue Shen, Tianhong Yan, Rui Nian und Amaury Lendasse. „Particle Swarm Optimization Based Selective Ensemble of Online Sequential Extreme Learning Machine“. Mathematical Problems in Engineering 2015 (2015): 1–10. http://dx.doi.org/10.1155/2015/504120.
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A novel particle swarm optimization based selective ensemble (PSOSEN) of online sequential extreme learning machine (OS-ELM) is proposed. It is based on the original OS-ELM with an adaptive selective ensemble framework. Two novel insights are proposed in this paper. First, a novel selective ensemble algorithm referred to as particle swarm optimization selective ensemble is proposed, noting that PSOSEN is a general selective ensemble method which is applicable to any learning algorithms, including batch learning and online learning. Second, an adaptive selective ensemble framework for online learning is designed to balance the accuracy and speed of the algorithm. Experiments for both regression and classification problems with UCI data sets are carried out. Comparisons between OS-ELM, simple ensemble OS-ELM (EOS-ELM), genetic algorithm based selective ensemble (GASEN) of OS-ELM, and the proposed particle swarm optimization based selective ensemble of OS-ELM empirically show that the proposed algorithm achieves good generalization performance and fast learning speed.
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Rahmawati, Eka, und Candra Agustina. „Implementasi Teknik Bagging untuk Peningkatan Kinerja J48 dan Logistic Regression dalam Prediksi Minat Pembelian Online“. Jurnal Teknologi Informasi dan Terapan 7, Nr. 1 (09.06.2020): 16–19. http://dx.doi.org/10.25047/jtit.v7i1.123.
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Abstract—The rapid growth of online shopping sites makes business in the virtual world very promising. Purchasing intentions is one of the keys to success in an online store. There are several data mining methods for making predictions on online purchase intentions datasets. Data can represent the characteristics or habits of each user who has visited a site whether it ends with a transaction or not. Some popular algorithms with good performance in data mining include J48 and Logistic Regression. However, in data sometimes there is a problem of class imbalance, so the ensemble technique needs to be applied. One technique that can be applied is bagging. This research examines data using bagging techniques to improve the performance of the J48 algorithm and Logistic Regression. The results of improving the performance of data mining algorithms with these techniques have an accuracy value of 89.68% for the J48 algorithm and 88.50% for the Logistic Regression algorithm. This figure shows an increase when compared with initial testing without using ensemble techniques. Increases were also experienced in Recall, F-Measure, and AUC values.
Keywords—purchasing intentions; J48; Logistic Regression; Bagging;
Abstrak— Pesatnya situs pembelanjaan online menjadikan bisnis di dunia virtual sangat menjanjikan. Minat pembelian menjadi salah satu kunci kesuksesan pada sebuah toko online. Terdapat beberapa metode data mining untuk melakukan prediksi pada dataset minat pembelian online. Data dapat mewakili karakteristik atau kebiasaan dari setiap user yang telah mengunjungi suatu situs baik berakhir dengan melakukan transaksi ataupun tidak. Beberapa algoritma yang populer dengan kinerja yang baik dalam data mining diantaranya J48 dan Logistic Regreession. Namun, dalam sebuah data terkadang terdapat masalah ketidakseimbangan kelas, sehingga perlu diterapkan teknik ensemble. Salah satu teknik yang dapat diterapkan adalah teknik bagging. Penelitian kali ini mengujikan data dengan teknik bagging untuk meningkatkan kinerja algoritma J48 dan Logistic Regression. Hasil dari peningkatan kinerja algoritma data mining dengan teknik tersebut memiliki nilai akurasi 89.68% untuk algoritma J48 dan 88.50% untuk algoritma Logistic Regression. Angka tersebut menunjukan adanya peningkatan jika dibandingkan dengan pengujian awal tanpa menggunakan teknik ensemble. Peningkatan juga dialami pada nilai Recall, F-Measure, dan AUC.
Keywords—Minat Pembelian, J48, Logistic Regression, Bagging
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Hansrajh, Arvin, Timothy T. Adeliyi und Jeanette Wing. „Detection of Online Fake News Using Blending Ensemble Learning“. Scientific Programming 2021 (28.07.2021): 1–10. http://dx.doi.org/10.1155/2021/3434458.
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The exponential growth in fake news and its inherent threat to democracy, public trust, and justice has escalated the necessity for fake news detection and mitigation. Detecting fake news is a complex challenge as it is intentionally written to mislead and hoodwink. Humans are not good at identifying fake news. The detection of fake news by humans is reported to be at a rate of 54% and an additional 4% is reported in the literature as being speculative. The significance of fighting fake news is exemplified during the present pandemic. Consequently, social networks are ramping up the usage of detection tools and educating the public in recognising fake news. In the literature, it was observed that several machine learning algorithms have been applied to the detection of fake news with limited and mixed success. However, several advanced machine learning models are not being applied, although recent studies are demonstrating the efficacy of the ensemble machine learning approach; hence, the purpose of this study is to assist in the automated detection of fake news. An ensemble approach is adopted to help resolve the identified gap. This study proposed a blended machine learning ensemble model developed from logistic regression, support vector machine, linear discriminant analysis, stochastic gradient descent, and ridge regression, which is then used on a publicly available dataset to predict if a news report is true or not. The proposed model will be appraised with the popular classical machine learning models, while performance metrics such as AUC, ROC, recall, accuracy, precision, and f1-score will be used to measure the performance of the proposed model. Results presented showed that the proposed model outperformed other popular classical machine learning models.
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Zhang, Junbo, C. Y. Chung und Lin Guan. „Noise Effect and Noise-Assisted Ensemble Regression in Power System Online Sensitivity Identification“. IEEE Transactions on Industrial Informatics 13, Nr. 5 (Oktober 2017): 2302–10. http://dx.doi.org/10.1109/tii.2017.2671351.
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Azeez, Nureni Ayofe, und Emad Fadhal. „Classification of Virtual Harassment on Social Networks Using Ensemble Learning Techniques“. Applied Sciences 13, Nr. 7 (04.04.2023): 4570. http://dx.doi.org/10.3390/app13074570.
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Background: Internet social media platforms have become quite popular, enabling a wide range of online users to stay in touch with their friends and relatives wherever they are at any time. This has led to a significant increase in virtual crime from the inception of these platforms to the present day. Users are harassed online when confidential information about them is stolen, or when another user posts insulting or offensive comments about them. This has posed a significant threat to online social media users, both mentally and psychologically. Methods: This research compares traditional classifiers and ensemble learning in classifying virtual harassment in online social media networks by using both models with four different datasets: seven machine learning algorithms (Nave Bayes NB, Decision Tree DT, K Nearest Neighbor KNN, Logistics Regression LR, Neural Network NN, Quadratic Discriminant Analysis QDA, and Support Vector Machine SVM) and four ensemble learning models (Ada Boosting, Gradient Boosting, Random Forest, and Max Voting). Finally, we compared our results using twelve evaluation metrics, namely: Accuracy, Precision, Recall, F1-measure, Specificity, Matthew’s Correlation Coefficient (MCC), Cohen’s Kappa Coefficient KAPPA, Area Under Curve (AUC), False Discovery Rate (FDR), False Negative Rate (FNR), False Positive Rate (FPR), and Negative Predictive Value (NPV) were used to show the validity of our algorithms. Results: At the end of the experiments, For Dataset 1, Logistics Regression had the highest accuracy of 0.6923 for machine learning algorithms, while Max Voting Ensemble had the highest accuracy of 0.7047. For dataset 2, K-Nearest Neighbor, Support Vector Machine, and Logistics Regression all had the same highest accuracy of 0.8769 in the machine learning algorithm, while Random Forest and Gradient Boosting Ensemble both had the highest accuracy of 0.8779. For dataset 3, the Support Vector Machine had the highest accuracy of 0.9243 for the machine learning algorithms, while the Random Forest ensemble had the highest accuracy of 0.9258. For dataset 4, the Support Vector Machine and Logistics Regression both had 0.8383, while the Max voting ensemble obtained an accuracy of 0.8280. A bar chart was used to represent our results, showing the minimum, maximum, and quartile ranges. Conclusions: Undoubtedly, this technique has assisted in no small measure in comparing the selected machine learning algorithms as well as the ensemble for detecting and exposing various forms of cyber harassment in cyberspace. Finally, the best and weakest algorithms were revealed.
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Bodyanskiy, Ye V., Kh V. Lipianina-Honcharenko und A. O. Sachenko. „ENSEMBLE OF ADAPTIVE PREDICTORS FOR MULTIVARIATE NONSTATIONARY SEQUENCES AND ITS ONLINE LEARNING“. Radio Electronics, Computer Science, Control, Nr. 4 (02.01.2024): 91. http://dx.doi.org/10.15588/1607-3274-2023-4-9.
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Context. In this research, we explore an ensemble of metamodels that utilizes multivariate signals to generate forecasts. The ensemble includes various traditional forecasting models such as multivariate regression, exponential smoothing, ARIMAX, as well as nonlinear structures based on artificial neural networks, ranging from simple feedforward networks to deep architectures like LSTM and transformers.
Objective. A goal of this research is to develop an effective method for combining forecasts from multiple models forming metamodels to create a unified forecast that surpasses the accuracy of individual models. We are aimed to investigate the effectiveness of the proposed ensemble in the context of forecasting tasks with nonstationary signals.
Method. The proposed ensemble of metamodels employs the method of Lagrange multipliers to estimate the parameters of the metamodel. The Kuhn-Tucker system of equations is solved to obtain unbiased estimates using the least squares method. Additionally, we introduce a recurrent form of the least squares algorithm for adaptive processing of nonstationary signals.
Results. The evaluation of the proposed ensemble method is conducted on a dataset of time series. Metamodels formed by combining various individual models demonstrate improved forecast accuracy compared to individual models. The approach shows effectiveness in capturing nonstationary patterns and enhancing overall forecasting accuracy.
Conclusions. The ensemble of metamodels, which utilizes multivariate signals for forecast generation, offers a promising approach to achieve better forecasting accuracy. By combining diverse models, the ensemble exhibits robustness to nonstationarity and improves the reliability of forecasts.
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R, Chitra A., und Dr Arjun B. C. „Performance Analysis of Regression Algorithms for Used Car Price Prediction: KNIME Analytics Platform“. International Journal for Research in Applied Science and Engineering Technology 11, Nr. 2 (28.02.2023): 1324–31. http://dx.doi.org/10.22214/ijraset.2023.49180.
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Abstract: In the recent years people’s willingness towards used car has increased. This has reflected in selling and buying of such cars. With the advance in technology online portal for marketing of used cars has come into effect. Many online portals focus to connect available used cars with user needs, present trends and various selection criteria. Using Machine Learning Algorithms such as Linear Regression, Tree Ensemble (Regression), Random forest (Regression), Gradient Boosted Tree(Regression), Simple Regression tree provided by KNIME Analytics Platform used car price predicted is performed. Analysis shows that Gradient Boosted Tree(Regression) prediction is closest to the target.
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Setiawan, Yahya, Jondri Jondri und Widi Astuti. „Twitter Sentiment Analysis on Online Transportation in Indonesia Using Ensemble Stacking“. JURNAL MEDIA INFORMATIKA BUDIDARMA 6, Nr. 3 (25.07.2022): 1452. http://dx.doi.org/10.30865/mib.v6i3.4359.
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Online transportation is a transportation innovation that has emerged along with the development of online-based applications that provide many features and conveniences. In its development, many users wrote their responses to the application on social media such as twitter. Many opinions and responses are directly conveyed by users of online transportation modes to their official accounts. The responses given by these users are very large and can be used as sentiment analysis on online transportation. However, the analysis process cannot be done manually. Therefore, we need a system that can help analyze user responses on Twitter automatically. In this study, a sentiment analysis system was built for online transportation in Indonesia using the ensemble stacking algorithm, which will simplify and increase the accuracy of the sentiment analysis. Ensemble stacking is a solution for advanced machine learning methods that can improve the performance of the base classifier. The system built on ensemble stacking uses three base classifiers, namely SVM kernel RBF, SVM linear kernel, and logistic regression. The best accuracy result on the gojek dataset is 88%, and the best F1 score is 87%. Ensemble Stacking which is applied to the research that the author conducted on online transportation sentiment analysis on twitter, obtained better accuracy than the base classifier used.
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de Almeida, Ricardo, Yee Mey Goh, Radmehr Monfared, Maria Teresinha Arns Steiner und Andrew West. „An ensemble based on neural networks with random weights for online data stream regression“. Soft Computing 24, Nr. 13 (09.11.2019): 9835–55. http://dx.doi.org/10.1007/s00500-019-04499-x.
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Abstract Most information sources in the current technological world are generating data sequentially and rapidly, in the form of data streams. The evolving nature of processes may often cause changes in data distribution, also known as concept drift, which is difficult to detect and causes loss of accuracy in supervised learning algorithms. As a consequence, online machine learning algorithms that are able to update actively according to possible changes in the data distribution are required. Although many strategies have been developed to tackle this problem, most of them are designed for classification problems. Therefore, in the domain of regression problems, there is a need for the development of accurate algorithms with dynamic updating mechanisms that can operate in a computational time compatible with today’s demanding market. In this article, the authors propose a new bagging ensemble approach based on neural network with random weights for online data stream regression. The proposed method improves the data prediction accuracy as well as minimises the required computational time compared to a recent algorithm for online data stream regression from literature. The experiments are carried out using four synthetic datasets to evaluate the algorithm’s response to concept drift, along with four benchmark datasets from different industries. The results indicate improvement in data prediction accuracy, effectiveness in handling concept drift, and much faster updating times compared to the existing available approach. Additionally, the use of design of experiments as an effective tool for hyperparameter tuning is demonstrated.
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Kothapalli. Mandakini, Et al. „Ensemble Learning for fraud detection in Online Payment System“. International Journal on Recent and Innovation Trends in Computing and Communication 11, Nr. 10 (02.11.2023): 1070–76. http://dx.doi.org/10.17762/ijritcc.v11i10.8626.
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The imbalanced problem in fraud detection systems refers to the unequal distribution of fraud cases and non-fraud cases in the information that is used to train machine learning models. This can make it difficult to accurately detect fraudulent activity. As a general rule, instances of fraud occur much less frequently than instances of other types of occurrences, which results in a dataset which is very unbalanced. This imbalance can present challenges for machine learning algorithms, as they may become biased towards the majority class (that is, non-fraud cases) and fail to accurately detect fraud. In situations like these, machine learning models may have a high accuracy overall, but a low recall for the minority class (i.e., fraud cases), which means that many instances of fraud will be misclassified as instances of something else and will not be found. In this study, Synthetic Minority Sampling Technique (SMOTE) is used for balancing the data set and the following machine learning algorithms such as decision trees, Enhanced logistic regression, Naive Bayes are used to classify the dataset.Majority Voting mechanism is used to ensemble the DT,NB, ELR methods and analyze the performance of the model. The performance of the Ensemble of various Machine Learning algorithms was superior to that of the other algorithms in terms of accuracy (98.62%), F1 score (95.21%), precision (98.02%), and recall (96.75%).
Dissertationen zum Thema "Online ensemble regression"
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Conesa, Gago Agustin. „Methods to combine predictions from ensemble learning in multivariate forecasting“. Thesis, Linnéuniversitetet, Institutionen för datavetenskap och medieteknik (DM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-103600.
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Making predictions nowadays is of high importance for any company, whether small or large, as thanks to the possibility to analyze the data available, new market opportunities can be found, risks and costs can be reduced, among others. Machine learning algorithms for time series can be used for predicting future values of interest. However, choosing the appropriate algorithm and tuning its metaparameters require a great level of expertise. This creates an adoption barrier for small and medium enterprises which could not afford hiring a machine learning expert to their IT team. For these reasons, this project studies different possibilities to make good predictions based on machine learning algorithms, but without requiring great theoretical knowledge from the users. Moreover, a software package that implements the prediction process has been developed. The software is an ensemble method that first predicts a value taking into account different algorithms at the same time, and then it combines their results considering also the previous performance of each algorithm to obtain a final prediction of the value. Moreover, the solution proposed and implemented in this project can also predict according to a concrete objective (e.g., optimize the prediction, or do not exceed the real value) because not every prediction problem is subject to the same constraints. We have experimented and validated the implementation with three different cases. In all of them, a better performance has been obtained in comparison with each of the algorithms involved, reaching improvements of 45 to 95%.
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Peng, Tao. „Analyse de données loT en flux“. Electronic Thesis or Diss., Aix-Marseille, 2021. http://www.theses.fr/2021AIXM0649.
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Depuis l'avènement de l'IoT (Internet of Things), nous assistons à une augmentation sans précédent du volume des données générées par des capteurs. Pour l'imputation des données manquantes d'un capteur f, nous proposons le modèle ISTM (Incremental Space-Time Model), qui utilise la régression linéaire multiple incrémentale adaptée aux données en flux non-stationnaires. ISTM met à jour son modèle en sélectionnant : 1) les données des capteurs voisins géographiquement du capteur f, et 2) les données les plus récentes retournées par f. Pour mesurer la confiance, nous proposons un modèle générique de prédiction DTOM (Data Trustworthiness Online Model) qui s'appuie sur des méthodes ensemblistes de régression en ligne comme AddExp et BNNRW . DTOM permet de prédire des valeurs de confiance en temps réel et comporte trois phases : 1) une phase d'initialisation du modèle, 2) une phase d'estimation du score de confiance, et 3) une phase de mise à jour heuristique du régresseur. Enfin, nous nous intéressons à la prédiction dans une STS avec des sorties multiples en présence de déséquilibre, c'est à dire lorsqu'il y a plus d'instances dans un intervalle de valeurs que dans un autre. Nous proposons MORSTS, une méthode de régression ensembliste en ligne, avec les caractéristiques suivantes : 1) les sous-modèles sont à sorties multiples, 2) l'utilisation de la stratégie sensible aux coûts c'est à dire que l'instance incorrectement prédite a un poids plus élevé, et 3) le contrôle du sur-apprentissage des sous-modèles naissants par la méthode de validation croisée k-fold. Des expérimentations avec des données réelles ont été effectuées et comparées avec des techniques connuesSince the advent of the IoT (Internet of Things), we have witnessed an unprecedented growth in the amount of data generated by sensors. To exploit this data, we first need to model it, and then we need to develop analytical algorithms to process it. For the imputation of missing data from a sensor f, we propose ISTM (Incremental Space-Time Model), an incremental multiple linear regression model adapted to non-stationary data streams. ISTM updates its model by selecting: 1) data from sensors located in the neighborhood of f, and 2) the near-past most recent data gathered from f. To evaluate data trustworthiness, we propose DTOM (Data Trustworthiness Online Model), a prediction model that relies on online regression ensemble methods such as AddExp (Additive Expert) and BNNRW (Bagging NNRW) for assigning a trust score in real time. DTOM consists: 1) an initialization phase, 2) an estimation phase, and 3) a heuristic update phase. Finally, we are interested predicting multiple outputs STS in presence of imbalanced data, i.e. when there are more instances in one value interval than in another. We propose MORSTS, an online regression ensemble method, with specific features: 1) the sub-models are multiple output, 2) adoption of a cost sensitive strategy i.e. the incorrectly predicted instance has a higher weight, and 3) management of over-fitting by means of k-fold cross-validation. Experimentation with with real data has been conducted and the results were compared with reknown techniques
Buchteile zum Thema "Online ensemble regression"
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Osojnik, Aljaž, Panče Panov und Sašo Džeroski. „iSOUP-SymRF: Symbolic Feature Ranking with Random Forests in Online Multi-target Regression“. In Discovery Science, 48–63. Cham: Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-45275-8_4.
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AbstractThe task of feature ranking has received considerable attention across various prediction tasks in the batch learning scenario, but not in the online learning setting. Available methods that estimate feature importances on data streams have thus far focused on ranking the features for the tasks of classification and occasionally multi-label classification. We propose a novel online feature ranking method for online multi-target regression, iSOUP-SymRF, which estimates feature importance scores based on the positions at which a feature appears in the trees of a random forest of iSOUP-Trees. By utilizing iSOUP-Trees, which can address multiple structured output prediction tasks on data streams, iSOUP-SymRF promises feature ranking across a variety of online structured output prediction tasks. We examine the robustness of iSOUP-SymRF and the feature rankings it produces in terms of the methods’ parameters: the size of the ensemble and the number of selected features. Furthermore, to show the utility of iSOUP-SymRF and its rankings we use them in conjunction with two state-of-the-art online multi-target regression methods, iSOUP-Tree and AMRules, and analyze the impact of adding features according to the rankings.
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Duda, Piotr, Maciej Jaworski und Leszek Rutkowski. „Online GRNN-Based Ensembles for Regression on Evolving Data Streams“. In Advances in Neural Networks – ISNN 2018, 221–28. Cham: Springer International Publishing, 2018. http://dx.doi.org/10.1007/978-3-319-92537-0_26.
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Nazeer, Ishrat, Mamoon Rashid, Sachin Kumar Gupta und Abhishek Kumar. „Use of Novel Ensemble Machine Learning Approach for Social Media Sentiment Analysis“. In Advances in Social Networking and Online Communities, 16–28. IGI Global, 2021. http://dx.doi.org/10.4018/978-1-7998-4718-2.ch002.
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Twitter is a platform where people express their opinions and come with regular updates. At present, it has become a source for many organizations where data will be extracted and then later analyzed for sentiments. Many machine learning algorithms are available for twitter sentiment analysis which are used for automatically predicting the sentiment of tweets. However, there are challenges that hinder machine learning classifiers to achieve better results in terms of classification. In this chapter, the authors are proposing a novel feature generation technique to provide desired features for training model. Next, the novel ensemble classification system is proposed for identifying sentiment in tweets through weighted majority rule ensemble classifier, which utilizes several commonly used statistical models like naive Bayes, random forest, logistic regression, which are weighted according to their performance on historical data, where weights are chosen separately for each model.
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Rajkumar S., Mary Nikitha K., Ramanathan L., Rajasekar Ramalingam und Mudit Jantwal. „Cloud Hosted Ensemble Learning-Based Rental Apartment Price Prediction Model Using Stacking Technique“. In Deep Learning Research Applications for Natural Language Processing, 229–38. IGI Global, 2022. http://dx.doi.org/10.4018/978-1-6684-6001-6.ch015.
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In this chapter, online rental listings of the city of Hyderabad are used as a data source for mapping house rent. Data points were scraped from one of the popular Indian rental websites www.nobroker.in. With the collected information, models of rental market dynamics were developed and evaluated using regression and boosting algorithms such as AdaBoost, CatBoost, LightGBM, XGBoost, KRR, ENet, and Lasso regression. An ensemble machine learning algorithm of the best combination of the aforementioned algorithms was also implemented using the stacking technique. The results of these algorithms were compared using several performance metrics such as coefficient of determination (R2 score), mean squared error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and accuracy in order to determine the most effective model. According to further examination of results, it is clear that the ensemble machine learning algorithm does outperform the others in terms of better accuracy and reduced errors.
Konferenzberichte zum Thema "Online ensemble regression"
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Kuzin, Danil, Le Yang, Olga Isupova und Lyudmila Mihaylova. „Ensemble Kalman Filtering for Online Gaussian Process Regression and Learning“. In 2018 International Conference on Information Fusion (FUSION). IEEE, 2018. http://dx.doi.org/10.23919/icif.2018.8455785.
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Xu, Jianpeng, Pang-Ning Tan und Lifeng Luo. „ORION: Online Regularized Multi-task Regression and Its Application to Ensemble Forecasting“. In 2014 IEEE International Conference on Data Mining (ICDM). IEEE, 2014. http://dx.doi.org/10.1109/icdm.2014.90.
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L. Grim, Luis Fernando, und Andre Leon S. Gradvohl. „High-Performance Ensembles of Online Sequential Extreme Learning Machine for Regression and Time Series Forecasting“. In 2018 30th International Symposium on Computer Architecture and High Performance Computing (SBAC-PAD). IEEE, 2018. http://dx.doi.org/10.1109/cahpc.2018.8645863.
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