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Journal articles on the topic 'Hybrid classifier'

Author: Grafiati

Published: 11 March 2023

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1

Demidova, Liliya, and Maksim Egin. "Data classification based on the hybrid intellectual technology." ITM Web of Conferences 18 (2018): 04001. http://dx.doi.org/10.1051/itmconf/20181804001.

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In this paper the data classification technique, implying the consistent application of the SVM and Parzen classifiers, has been suggested. The Parser classifier applies to data which can be both correctly and erroneously classified using the SVM classifier, and are located in the experimentally defined subareas near the hyperplane which separates the classes. A herewith, the SVM classifier is used with the default parameters values, and the optimal parameters values of the Parser classifier are determined using the genetic algorithm. The experimental results confirming the effectiveness of the proposed hybrid intellectual data classification technology have been presented.

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YOUNG KOO, JA, and MYUNGHWAN KIM. "An improved hybrid classifier." International Journal of Remote Sensing 7, no. 3 (March 1986): 471–76. http://dx.doi.org/10.1080/01431168608954702.

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Zhiwen Yu, Le Li, Jiming Liu, and Guoqiang Han. "Hybrid Adaptive Classifier Ensemble." IEEE Transactions on Cybernetics 45, no. 2 (February 2015): 177–90. http://dx.doi.org/10.1109/tcyb.2014.2322195.

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Sharma, Richa, and Shailendra Narayan Singh. "An Efficient Hybrid Classifier for Prognosing Cardiac Disease." Webology 19, no. 1 (January 20, 2022): 5028–46. http://dx.doi.org/10.14704/web/v19i1/web19338.

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Machine learning (ML) is a powerful tool which empowers the practitioners for predictions upon any existing or real- time data. Here, the Machine first understands the valuable patterns from the dataset and then uses that information to make predictions on the unknown data. Further, classification is the commonly used machine learning approach (ML-Approach) to make such predictions. The objective of this work aims to design and development of an ensemble classifier for prognosing cardiovascular disease (heart disease). The developed classifier integrates Support Vector Machine (SVM), K–Nearest Neighbor (K-NN), and Weighted K-NN. The applicability of ensemble classifier is evaluated on the Cleveland Heart disease dataset. Some other classifiers such as Logistic Regression (LR), Sequential Minimal Optimization (SMO), K-NN+Weighted K-NN are also implemented on the same dataset to make the performance analysis. The results of this study depict the significant improvement in the Sensitivity and Specificity parameter.

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Kotsiantis, Sotiris. "A hybrid decision tree classifier." Journal of Intelligent & Fuzzy Systems 26, no. 1 (2014): 327–36. http://dx.doi.org/10.3233/ifs-120741.

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Yu, Zhiwen, Hantao Chen, Jiming Liuxs, Jane You, Hareton Leung, and Guoqiang Han. "Hybrid $k$ -Nearest Neighbor Classifier." IEEE Transactions on Cybernetics 46, no. 6 (June 2016): 1263–75. http://dx.doi.org/10.1109/tcyb.2015.2443857.

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Demidova, Liliya A. "Two-Stage Hybrid Data Classifiers Based on SVM and kNN Algorithms." Symmetry 13, no. 4 (April 7, 2021): 615. http://dx.doi.org/10.3390/sym13040615.

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The paper considers a solution to the problem of developing two-stage hybrid SVM-kNN classifiers with the aim to increase the data classification quality by refining the classification decisions near the class boundary defined by the SVM classifier. In the first stage, the SVM classifier with default parameters values is developed. Here, the training dataset is designed on the basis of the initial dataset. When developing the SVM classifier, a binary SVM algorithm or one-class SVM algorithm is used. Based on the results of the training of the SVM classifier, two variants of the training dataset are formed for the development of the kNN classifier: a variant that uses all objects from the original training dataset located inside the strip dividing the classes, and a variant that uses only those objects from the initial training dataset that are located inside the area containing all misclassified objects from the class dividing strip. In the second stage, the kNN classifier is developed using the new training dataset above-mentioned. The values of the parameters of the kNN classifier are determined during training to maximize the data classification quality. The data classification quality using the two-stage hybrid SVM-kNN classifier was assessed using various indicators on the test dataset. In the case of the improvement of the quality of classification near the class boundary defined by the SVM classifier using the kNN classifier, the two-stage hybrid SVM-kNN classifier is recommended for further use. The experimental results approve the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem. The experimental results obtained with the application of various datasets confirm the feasibility of using two-stage hybrid SVM-kNN classifiers in the data classification problem.

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Liu, Su Houn, Hsiu Li Liao, Shih Ming Pi, and Chih Chiang Kao. "Patent Classification Using Hybrid Classifier Systems." Advanced Materials Research 187 (February 2011): 458–63. http://dx.doi.org/10.4028/www.scientific.net/amr.187.458.

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Patents are distributed through hundreds of collections, divided up by general area. A hybrid classifier system thus can be a powerful solution to difficult patent classification problems. In this study, we present a system for classifying patent documents on a hybrid approach by combining multiple text classifiers (Naïve Bayes, KNN and Rocchio). Decisions made by various text classifiers can be combined by voting and sampling mechanisms in the system. A prototype system was developed and tested in a real world task. The results have indicated that the accuracy of the hybrid approach is more stable than that of any of the three individual text classifiers.

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Rangel-Díaz-de-la-Vega, Adolfo, Yenny Villuendas-Rey, Cornelio Yáñez-Márquez, Oscar Camacho-Nieto, and Itzamá López-Yáñez. "Impact of Imbalanced Datasets Preprocessing in the Performance of Associative Classifiers." Applied Sciences 10, no. 8 (April 16, 2020): 2779. http://dx.doi.org/10.3390/app10082779.

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In this paper, an experimental study was carried out to determine the influence of imbalanced datasets preprocessing in the performance of associative classifiers, in order to find the better computational solutions to the problem of credit scoring. To do this, six undersampling algorithms, six oversampling algorithms and four hybrid algorithms were evaluated in 13 imbalanced datasets referring to credit scoring. Then, the performance of four associative classifiers was analyzed. The experiments carried out allowed us to determine which sampling algorithms had the best results, as well as their impact on the associative classifiers evaluated. Accordingly, we determine that the Hybrid Associative Classifier with Translation, the Extended Gamma Associative Classifier and the Naïve Associative Classifier do not improve their performance by using sampling algorithms for credit data balancing. On the other hand, the Smallest Normalized Difference Associative Memory classifier was beneficiated by using oversampling and hybrid algorithms.

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Anagnostopoulos, Theodoros, and Christos Skourlas. "Ensemble majority voting classifier for speech emotion recognition and prediction." Journal of Systems and Information Technology 16, no. 3 (August 5, 2014): 222–32. http://dx.doi.org/10.1108/jsit-01-2014-0009.

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Purpose – The purpose of this paper is to understand the emotional state of a human being by capturing the speech utterances that are used during common conversation. Human beings except of thinking creatures are also sentimental and emotional organisms. There are six universal basic emotions plus a neutral emotion: happiness, surprise, fear, sadness, anger, disgust and neutral. Design/methodology/approach – It is proved that, given enough acoustic evidence, the emotional state of a person can be classified by an ensemble majority voting classifier. The proposed ensemble classifier is constructed over three base classifiers: k nearest neighbors, C4.5 and support vector machine (SVM) polynomial kernel. Findings – The proposed ensemble classifier achieves better performance than each base classifier. It is compared with two other ensemble classifiers: one-against-all (OAA) multiclass SVM with radial basis function kernels and OAA multiclass SVM with hybrid kernels. The proposed ensemble classifier achieves better performance than the other two ensemble classifiers. Originality/value – The current paper performs emotion classification with an ensemble majority voting classifier that combines three certain types of base classifiers which are of low computational complexity. The base classifiers stem from different theoretical background to avoid bias and redundancy. It gives to the proposed ensemble classifier the ability to be generalized in the emotion domain space.

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Chang, Yangyang, and Fadi Abu-Amara. "An Efficient Hybrid Classifier for Cancer Detection." International Journal of Online and Biomedical Engineering (iJOE) 17, no. 03 (March 9, 2021): 76. http://dx.doi.org/10.3991/ijoe.v17i03.19683.

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<span>The early detection of cancer in both healthy and high-risk populations offers increased opportunity for treatment and curative intent. In this paper, we propose a hybrid classifier that produces an efficient classification system for cancer detection in cell datasets. The first part of this work investigates the performance of artificial neural networks (ANN) such as Self-Organizing Feature Map (SOM) and Learning Vector Quantization (LVQ), while in the second part, we present our investigation on the performances of Decision Tree (DT) and its pruning model. We also, in the third part, present our proposal for a new hybrid classifier that is based on the Random Forest (RF) and the combination of the LVQ and DT. Experimental results of the proposed hybrid classifier indicate that the hybrid classifier effectively avoids the drawbacks of individual classifiers and has high anti-noise performance.</span>

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Gill, Harmandeep Singh, and Baljit Singh Khehra. "Hybrid classifier model for fruit classification." Multimedia Tools and Applications 80, no. 18 (May 21, 2021): 27495–530. http://dx.doi.org/10.1007/s11042-021-10772-9.

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Adivarekar, Pranali Ramesh. "Diabetic Retinopathy Detection using Hybrid Classifier." International Journal for Research in Applied Science and Engineering Technology 7, no. 8 (August 31, 2019): 133–38. http://dx.doi.org/10.22214/ijraset.2019.8017.

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Yang, Kaixiang, Zhiwen Yu, Xin Wen, Wenming Cao, C. L. Philip Chen, Hau-San Wong, and Jane You. "Hybrid Classifier Ensemble for Imbalanced Data." IEEE Transactions on Neural Networks and Learning Systems 31, no. 4 (April 2020): 1387–400. http://dx.doi.org/10.1109/tnnls.2019.2920246.

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Saradha, S., and P. Sujatha. "Prediction of gestational diabetes diagnosis using SVM and J48 classifier model." International Journal of Engineering & Technology 7, no. 2.21 (April 20, 2018): 323. http://dx.doi.org/10.14419/ijet.v7i2.21.12395.

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Knowledge Discovery in Databases (KDD) process is also known as data mining. It is a most powerful tool for medical diagnosis. Due to hormonal changes, diabetes may occur during pregnancy is referred as Gestational diabetes mellitus (GDM). Pregnant Women with GDM are at highest risk of future diabetes, especially type-2 diabetes. This paper focuses on designing an automated system for diagnosing gestational diabetes using hybrid classifiers as well as predicting the highest risk factors of getting Type 2 diabetes after delivery. One of the common predictive data mining tasks is classification. It classifies the data and builds a model based on the test data values and attributes to produce the new classified data. For detecting GDM and also its risk factors, two classifier models namely modified SVM and modified J48 classifier models are proposed. The data set were collected from various hospitals and clinical labs and preprocessed with discretize filter using weka tool. Missing values are replaced by the suitable values. The final preprocessed data applied in the proposed classifier Model. The output of the proposed model is compared with all the other existing methodologies. Since the proposed model modified J48 classifier model produces more accuracy and low error rate against other existing classifier models.

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Ruppert, Georg S., Mathias Schardt, Gerd Balzuweit, and Mushtaq Hussain. "A Hybrid Classifier for Remote Sensing Applications." International Journal of Neural Systems 08, no. 01 (February 1997): 63–68. http://dx.doi.org/10.1142/s0129065797000094.

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This paper presents a hybrid — unsupervised and supervised — classifier for land use classification of remote sensing images. The entire satellite image is quantized by an unsupervised Neural Gas process and the resulting codebook is labeled by a supervised majority voting process using the ground truth. The performance of the classifier is similar to that of Maximum Likelihood and is only a little worse than Multilayer Perceptrons while training and classifying requires no expert knowledge after collecting the ground truth. The hybrid classifier is much better suited to classifications with complex non-normally distributed classes than Maximum Likelihood. The main advantage of the Neural Gas classifier, however, is that it requires much less user interaction than other classifier, especially Maximum Likelihood.

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Hartono, Hartono, Erianto Ongko, and Dahlan Abdullah. "Hybrid approach redefinition with cluster-based instance selection in handling class imbalance problem." International Journal of Advances in Intelligent Informatics 7, no. 3 (November 30, 2021): 345. http://dx.doi.org/10.26555/ijain.v7i3.515.

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Class Imbalance problems often occur in the classification process, the existence of these problems is characterized by the tendency of a class to have instances that are much larger than other classes. This problem certainly causes a tendency towards low accuracy in minority classes with smaller number of instances and also causes important information on minority classes not to be obtained. Various methods have been applied to overcome the problem of the imbalance class. One of them is the Hybrid Approach Redefinition method which is one of the Hybrid Ensembles methods. The tendency to pay attention to the performance classifier, has led to an understanding of the importance of selecting an instance that will be used as a classifier. In the classic Hybrid Approach Redefinition method classifier selection is done randomly using the Random Under Sampling approach, and it is interesting to study how performance is obtained if the sampling process is based on Cluster-Based by selecting existing instances. The purpose of this study is to apply the Hybrid Approach Redefinition method with Cluster-Based Instance Selection (CBIS) approach so that it can obtain a better performance classifier. The results showed that Hybrid Approach Redefinition with cluster-based instance selection gave better results on the number of classifiers, data diversity, and performance classifiers compared to classic Hybrid Approach Redefinition.

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Wang, Wenbo, Lu Chen, Ming Tan, Shaojun Wang, and Amit P. Sheth. "Discovering Fine-grained Sentiment in Suicide Notes." Biomedical Informatics Insights 5s1 (January 2012): BII.S8963. http://dx.doi.org/10.4137/bii.s8963.

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This paper presents our solution for the i2b2 sentiment classification challenge. Our hybrid system consists of machine learning and rule-based classifiers. For the machine learning classifier, we investigate a variety of lexical, syntactic and knowledge-based features, and show how much these features contribute to the performance of the classifier through experiments. For the rule-based classifier, we propose an algorithm to automatically extract effective syntactic and lexical patterns from training examples. The experimental results show that the rule-based classifier outperforms the baseline machine learning classifier using unigram features. By combining the machine learning classifier and the rule-based classifier, the hybrid system gains a better trade-off between precision and recall, and yields the highest micro-averaged F-measure (0.5038), which is better than the mean (0.4875) and median (0.5027) micro-average F-measures among all participating teams.

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Paul, Jis, and M. Madheswaran. "Hybrid Neuro-Fuzzy Learning Models for Classification of Motion Sickness Levels Using Biosignals." Journal of Medical Imaging and Health Informatics 11, no. 11 (November 1, 2021): 2790–805. http://dx.doi.org/10.1166/jmihi.2021.3871.

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Motion sickness is all around as long as there is existence of humans and motion. This sickness has been common in numerous people and due to which it has become the focus area of neurological, psychological and physiological researchers. Most common group of this motion sickness pertains to the category of visual sensitivity; also called visual dependence, wherein people become sick due to visual motion. In this research paper, classification of the levels of motion sickness is done by developing classifiers: (1) k-Nearest neighbour (kNN) classifier (2) Fuzzy c-means classifier (3) ELMAN neural classifier (4) Fuzzy-Wavelet neural network classifier. All the developed classifier models are based on variants of machine learning approaches and are designed to overcome the limitation of the conventional binary classification approach. In this work, electroencephalogram (EEG) data, centre of pressure and trajectories of head and waist motion data of 20 people were recorded and the developed classifier models were applied over them to attain the classification accuracy. Features of these multiple biosignals are denoised and extracted over which the classifier models were tested. The proposed technique is simulated in MATLAB simulation environment for the considered candidate data samples. Numerical simulation was carried out and the results prove the superiority and effectiveness of the developed classifiers over the various existing classifier models.

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Nalluri, MadhuSudana Rao, Kannan K., Manisha M., and Diptendu Sinha Roy. "Hybrid Disease Diagnosis Using Multiobjective Optimization with Evolutionary Parameter Optimization." Journal of Healthcare Engineering 2017 (2017): 1–27. http://dx.doi.org/10.1155/2017/5907264.

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With the widespread adoption of e-Healthcare and telemedicine applications, accurate, intelligent disease diagnosis systems have been profoundly coveted. In recent years, numerous individual machine learning-based classifiers have been proposed and tested, and the fact that a single classifier cannot effectively classify and diagnose all diseases has been almost accorded with. This has seen a number of recent research attempts to arrive at a consensus using ensemble classification techniques. In this paper, a hybrid system is proposed to diagnose ailments using optimizing individual classifier parameters for two classifier techniques, namely, support vector machine (SVM) and multilayer perceptron (MLP) technique. We employ three recent evolutionary algorithms to optimize the parameters of the classifiers above, leading to six alternative hybrid disease diagnosis systems, also referred to as hybrid intelligent systems (HISs). Multiple objectives, namely, prediction accuracy, sensitivity, and specificity, have been considered to assess the efficacy of the proposed hybrid systems with existing ones. The proposed model is evaluated on 11 benchmark datasets, and the obtained results demonstrate that our proposed hybrid diagnosis systems perform better in terms of disease prediction accuracy, sensitivity, and specificity. Pertinent statistical tests were carried out to substantiate the efficacy of the obtained results.

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Garfield, Sheila, Stefan Wermter, and Siobhan Devlin. "Spoken language classification using hybrid classifier combination." International Journal of Hybrid Intelligent Systems 2, no. 1 (June 14, 2005): 13–33. http://dx.doi.org/10.3233/his-2005-2102.

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Qiu, Dahong, Ye Wang, and Bin Bi. "Identify Cross-Selling Opportunities via Hybrid Classifier." International Journal of Data Warehousing and Mining 4, no. 2 (April 2008): 55–62. http://dx.doi.org/10.4018/jdwm.2008040107.

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Kim, Young-Won, and Il-Seok Oh. "Hybrid Genetic Algorithm for Classifier Ensemble Selection." KIPS Transactions:PartB 14B, no. 5 (October 31, 2007): 369–76. http://dx.doi.org/10.3745/kipstb.2007.14-b.5.369.

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Kumar, Uttam, S. Kumar Raja, Chiranjit Mukhopadhyay, and T. V. Ramachandra. "Hybrid Bayesian Classifier for Improved Classification Accuracy." IEEE Geoscience and Remote Sensing Letters 8, no. 3 (May 2011): 474–77. http://dx.doi.org/10.1109/lgrs.2010.2087006.

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Kim, Young-Won, and Il-Seok Oh. "Classifier ensemble selection using hybrid genetic algorithms." Pattern Recognition Letters 29, no. 6 (April 2008): 796–802. http://dx.doi.org/10.1016/j.patrec.2007.12.013.

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Wen, Hui, Hongguang Fan, Weixin Xie, and Jihong Pei. "Hybrid Structure-Adaptive RBF-ELM Network Classifier." IEEE Access 5 (2017): 16539–54. http://dx.doi.org/10.1109/access.2017.2740420.

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Kim, Jin-Chul, Min-Hyun Kim, Han-Enul Suh, Muhammad Tahir Naseem, and Chan-Su Lee. "Hybrid Approach for Facial Expression Recognition Using Convolutional Neural Networks and SVM." Applied Sciences 12, no. 11 (May 28, 2022): 5493. http://dx.doi.org/10.3390/app12115493.

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Facial expression recognition is very useful for effective human–computer interaction, robot interfaces, and emotion-aware smart agent systems. This paper presents a new framework for facial expression recognition by using a hybrid model: a combination of convolutional neural networks (CNNs) and a support vector machine (SVM) classifier using dynamic facial expression data. In order to extract facial motion characteristics, dense facial motion flows and geometry landmark flows of facial expression sequences were used as inputs to the CNN and SVM classifier, respectively. CNN architectures for facial expression recognition from dense facial motion flows were proposed. The optimal weighting combination of the hybrid classifiers provides better facial expression recognition results than individual classifiers. The system has successfully classified seven facial expressions signalling anger, contempt, disgust, fear, happiness, sadness and surprise classes for the CK+ database, and facial expressions of anger, disgust, fear, happiness, sadness and surprise for the BU4D database. The recognition performance of the proposed system is 99.69% for the CK+ database and 94.69% for the BU4D database. The proposed method shows state-of-the-art results for the CK+ database and is proven to be effective for the BU4D database when compared with the previous schemes.

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Govindarajan, M., and RM Chandrasekaran. "A Hybrid Multilayer Perceptron Neural Network for Direct Marketing." International Journal of Knowledge-Based Organizations 2, no. 3 (July 2012): 63–73. http://dx.doi.org/10.4018/ijkbo.2012070104.

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Data Mining is the use of algorithms to extract the information and patterns derived by the knowledge discovery in database process. It is often referred to as supervised learning because the classes are determined before examining the data. In many data mining applications that address classification problems, feature and model selection are considered as key tasks. That is, appropriate input features of the classifier must be selected from a given set of possible features and structure parameters of the classifier must be adapted with respect to these features and a given data set. This paper describes feature selection and model selection simultaneously for Multilayer Perceptron (MLP) classifiers. In order to reduce the optimization effort, various techniques are integrated that accelerate and improve the classifier significantly. The feasibility and the benefits of the proposed approach are demonstrated by means of data mining problem: Direct Marketing in Customer Relationship Management. It is shown that, compared to earlier MLP technique, the run time is reduced with respect to learning data and with validation data for the proposed Multilayer Perceptron (MLP) classifiers. Similarly, the error rate is relatively low with respect to learning data and with validation data in direct marketing dataset. The algorithm is independent of specific applications so that many ideas and solutions can be transferred to other classifier paradigms.

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Karaca, Yunus Emre, and Serpil Aslan. "Auto-Diagnosis of Lung Cancer with the Proposed Feature Fusion-Based Hybrid Deep Model." Review of Computer Engineering Studies 9, no. 3 (September 30, 2022): 87–93. http://dx.doi.org/10.18280/rces.090301.

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Early detection of lung cancer increases the response rate to treatment. Therefore, the accuracy of diagnostic methods is of great importance. Reading the patient's medical images by radiologists can cause a severe time cost besides subjective result. In this context, Artificial Intelligence (AI) methods create an innovative field to reduce the workforce of radiologists and obtain objective results. AI methods play a vital role in improving the analysis of the dataset, extracting meaningful features, clustering, and classification. In our study, the data set contains healthy images besides CT images of malignant and benign tumors with lung cancer; AlexNet is trained using DenseNet 201, GoogleNet, MobileNetV2, and ResNet50 architectures. In addition, a hybrid model has been developed to classify lung CT images. The developed model constitutively used GoogleNet, MobileNetV2, and ResNet50 architectures. The feature maps obtained in these three architectures were combined and classified into different classifiers. Among the classifiers used in the study, the highest accuracy rate was achieved in the Ensemble Subspace KNN classifier. The accuracy value obtained in this classifier is 98.3%.

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Sampath, A. K., and N. Gomathi. "Probabilistic Model Based Hybrid Classifier for Character Recognition." International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems 25, no. 04 (July 14, 2017): 621–47. http://dx.doi.org/10.1142/s0218488517500271.

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Handwritten character recognition is most crucial one indulging in many of the applications like forensic search, searching historical manuscripts, mail sorting, bank check reading, tax form processing, book and handwritten notes transcription etc. The problem occurrence in the recognition is mainly because of the writing style variation, size variation (length and height), orientation angle etc. In this paper a probabilistic model based hybrid classifier is proposed for the character recognition combining the neural network and decision tree classifiers. In addition to the local gradient features i.e. histogram oriented feature and grid level feature, an additional feature called GLCM feature is extracted from the input image in the proposed recognition system and are concatenated for the image recognition procedure to encode color, shape, texture, local as well as the statistical information. These extracted features considered are given to the hybrid classifier which recognises the character. In the test set, recognition accuracy of 95% is achieved. The proposed probabilistic model based hybrid classifier tends to contribute more accurate character recognition rate compared to the existing character recognition system.

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Rathore, Pramod Singh, Jyotir Moy Chatterjee, Abhishek Kumar, and Aakanksha Jain. "An assessment of classification with hybrid methodology for neural network classifier against different classifier." International Journal of Collaborative Intelligence 2, no. 2 (2020): 83. http://dx.doi.org/10.1504/ijci.2020.10033865.

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Jain, Aakanksha, Abhishek Kumar, Jyotir Moy Chatterjee, and Pramod Singh Rathore. "An assessment of classification with hybrid methodology for neural network classifier against different classifier." International Journal of Collaborative Intelligence 2, no. 2 (2020): 83. http://dx.doi.org/10.1504/ijci.2020.111659.

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Abdullah, Muhammed Amin, Yongbin Yu, Kwabena Adu, Yakubu Imrana, Xiangxiang Wang, and Jingye Cai. "HCL-Classifier: CNN and LSTM based hybrid malware classifier for Internet of Things (IoT)." Future Generation Computer Systems 142 (May 2023): 41–58. http://dx.doi.org/10.1016/j.future.2022.12.034.

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Dr. M. Renuka Devi, Thahira Banu V. ,. "HYBRID CLASSIFIER TO CLASSIFY THE FINGER NAIL ABNORMALITIES." INFORMATION TECHNOLOGY IN INDUSTRY 9, no. 1 (March 5, 2021): 549–55. http://dx.doi.org/10.17762/itii.v9i1.168.

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Nail diagnosis is a method to predict the possibilities of organ failures and various systemic diseases. Nail abnormalities are considered as the signs of certain diseases in traditional medicines such as Siddha Medicine, Ayurveda, Yunani and Chinese medicine etc. In this paper, the performance of existing techniques such as SVM classifier and KNN classifiers are compared with the proposed method. The metrics precision, recall, F-measure and accuracy are calculated and compared. The 100 images had taken for study and the proposed novel segmentation method gives the best accuracy. The experiment uses 480 (increase the dataset) images of eight types of abnormalities. 70% of images were used for training and 30% of images were used for testing. (Discuss the performance measure)

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Sabena, S., L. Sai Ramesh, and K. Selva Kumar. "Multiple Cancer's Prediction using Hybrid Naïve Baye's Classifier." Asian Journal of Research in Social Sciences and Humanities 6, no. 6 (2016): 1770. http://dx.doi.org/10.5958/2249-7315.2016.00325.7.

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NGUYEN, Xuan-Dao, Mun-Ho JEONG, Bum-Jae YOU, and Sang-Rok OH. "Self-Taught Classifier of Gateways for Hybrid SLAM." IEICE Transactions on Communications E93-B, no. 9 (2010): 2481–84. http://dx.doi.org/10.1587/transcom.e93.b.2481.

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Ahlawat, Savita, and Amit Choudhary. "Hybrid CNN-SVM Classifier for Handwritten Digit Recognition." Procedia Computer Science 167 (2020): 2554–60. http://dx.doi.org/10.1016/j.procs.2020.03.309.

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Prevost, Lionel, Loïc Oudot, Alvaro Moises, Christian Michel-Sendis, and Maurice Milgram. "Hybrid generative/discriminative classifier for unconstrained character recognition." Pattern Recognition Letters 26, no. 12 (September 2005): 1840–48. http://dx.doi.org/10.1016/j.patrec.2005.03.005.

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Osman Abda, Saad, Safaai Deris ., and Mohd Saberi Mohamad . "A Hybrid Classifier for Protein Secondary Structure Prediction." Information Technology Journal 4, no. 4 (September 15, 2005): 433–38. http://dx.doi.org/10.3923/itj.2005.433.438.

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KavithaRani, B., and A. Govardhan. "Effective Features and Hybrid Classifier for Rainfall Prediction." International Journal of Computational Intelligence Systems 7, no. 5 (September 3, 2014): 937–51. http://dx.doi.org/10.1080/18756891.2014.960234.

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Lee, Jae Sik, and Jong Gu Kwon. "A Hybrid SVM Classifier for Imbalanced Data Sets." Journal of Intelligence and Information Systems 19, no. 2 (June 30, 2013): 125–40. http://dx.doi.org/10.13088/jiis.2013.19.2.125.

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Souidi, Mohamed Abdou, and Noria Taghezout. "Privacy Protection in Enterprise Social Networks Using a Hybrid De-Identification System." International Journal of Information Security and Privacy 15, no. 1 (January 2021): 138–52. http://dx.doi.org/10.4018/ijisp.2021010107.

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Enterprise social networks (ESN) have been widely used within organizations as a communication infrastructure that allows employees to collaborate with each other and share files and documents. The shared documents may contain a large amount of sensitive information that affect the privacy of persons such as phone numbers, which must be protected against any kind of disclosure or unauthorized access. In this study, authors propose a hybrid de-identification system that extract sensitive information from textual documents shared in ESNs. The system is based on both machine learning and rule-based classifiers. Gradient boosted trees (GBTs) algorithm is used as machine learning classifier. Experiments ran on a modified CoNLL 2003 dataset show that GBTs algorithm achieve a very high F1-score (95%). Additionally, the rule-based classifier is consisted of regular expression and gazetteers in order to complement the machine learning classifier. Thereafter, the sensitive information extracted by the two classifiers are merged and encrypted using Format Preserving Encryption method.

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Varga, Michal, Ján Jadlovský, and Slávka Jadlovská. "Generative Enhancement of 3D Image Classifiers." Applied Sciences 10, no. 21 (October 22, 2020): 7433. http://dx.doi.org/10.3390/app10217433.

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In this paper, we propose a methodology for generative enhancement of existing 3D image classifiers. This methodology is based on combining the advantages of both non-generative classifiers and generative modeling. Its purpose is to streamline the synthesis of novel deep neural networks by embedding existing compatible classifiers into a generative network architecture. A demonstration of this process and evaluation of its effectiveness is performed using a 3D convolutional classifier and its generative equivalent—a 3D conditional generative adversarial network classifier. The results of the experiments show that the generative classifier delivers higher performance, gaining a relative classification accuracy improvement of 7.43%. An increase of accuracy is also observed when comparing it to a plain convolutional classifier that was trained on a dataset augmented with samples created by the trained generator. This suggests a desirable knowledge sharing mechanism exists within the hybrid discriminator-classifier network.

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Novoselova, Natalia, and Igor Tom. "Hybrid Classification Model for Biomedical Data Analysis." Information Technology and Management Science 25 (December 9, 2022): 16–23. http://dx.doi.org/10.7250/itms-2022-0003.

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The paper describes a method for constructing a hybrid classification model that allows combining several sources of biological information in order to build a classifier to identify subtypes of complex diseases. The distinctive feature of the method is its adaptive nature, i.e. the ability to build efficient classifiers regardless of data types, as well as a multi-criteria approach to evaluate the effectiveness of a classification. The testing results on real biomedical data showed the advantages of the proposed hybrid model in comparison with individual classifiers.

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N, Komal Kumar, R. Lakshmi Tulasi, and Vigneswari D. "An ensemble multi-model technique for predicting chronic kidney disease." International Journal of Electrical and Computer Engineering (IJECE) 9, no. 2 (April 1, 2019): 1321. http://dx.doi.org/10.11591/ijece.v9i2.pp1321-1326.

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<span lang="EN-US">Chronic Kidney Disease (CKD) is a type of lifelong kidney disease that leads to the gradual loss of kidney function over time; the main function of the kidney is to filter the wastein the human body. When the kidney malfunctions, the wastes accumulate in our body leading to complete failure. Machine learning algorithms can be used in prediction of the kidney disease at early stages by analyzing the symptoms. The aim of this paper is to propose an ensemble learning technique for predicting Chronic Kidney Disease (CKD). We propose a new hybrid classifier called as ABC4.5, which is ensemble learning for predicting Chronic Kidney Disease (CKD). The proposed hybrid classifier is compared with the machine learning classifiers such as Support Vector Machine (SVM), Decision Tree (DT), C4.5, Particle Swarm Optimized Multi Layer Perceptron (PSO-MLP). The proposed classifier accurately predicts the occurrences of kidney disease by analysis various medical factors. The work comprises of two stages, the first stage consists of obtaining weak decision tree classifiers from C4.5 and in the second stage, the weak classifiers are added to the weighted sum to represent the final output for improved performance of the classifier.</span>

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JACKOWSKI, KONRAD, BARTOSZ KRAWCZYK, and MICHAŁ WOŹNIAK. "IMPROVED ADAPTIVE SPLITTING AND SELECTION: THE HYBRID TRAINING METHOD OF A CLASSIFIER BASED ON A FEATURE SPACE PARTITIONING." International Journal of Neural Systems 24, no. 03 (February 19, 2014): 1430007. http://dx.doi.org/10.1142/s0129065714300071.

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Currently, methods of combined classification are the focus of intense research. A properly designed group of combined classifiers exploiting knowledge gathered in a pool of elementary classifiers can successfully outperform a single classifier. There are two essential issues to consider when creating combined classifiers: how to establish the most comprehensive pool and how to design a fusion model that allows for taking full advantage of the collected knowledge. In this work, we address the issues and propose an AdaSS+, training algorithm dedicated for the compound classifier system that effectively exploits local specialization of the elementary classifiers. An effective training procedure consists of two phases. The first phase detects the classifier competencies and adjusts the respective fusion parameters. The second phase boosts classification accuracy by elevating the degree of local specialization. The quality of the proposed algorithms are evaluated on the basis of a wide range of computer experiments that show that AdaSS+ can outperform the original method and several reference classifiers.

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Muqasqas, Saed A., Qasem A. Al Radaideh, and Bilal A. Abul-Huda. "A Hybrid Classification Approach Based on Decision Tree and Naïve Bays Methods." International Journal of Information Retrieval Research 4, no. 4 (October 2014): 61–72. http://dx.doi.org/10.4018/ijirr.2014100104.

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Data classification as one of the main tasks of data mining has an important role in many fields. Classification techniques differ mainly in the accuracy of their models, which depends on the method adopted during the learning phase. Several researchers attempted to enhance the classification accuracy by combining different classification methods in the same learning process; resulting in a hybrid-based classifier. In this paper, the authors propose and build a hybrid classifier technique based on Naïve Bayes and C4.5 classifiers. The main goal of the proposed model is to reduce the complexity of the NBTree technique, which is a well known hybrid classification technique, and to improve the overall classification accuracy. Thirty six samples of UCI datasets were used in evaluation. Results have shown that the proposed technique significantly outperforms the NBTree technique and some other classifiers proposed in the literature in term of classification accuracy. The proposed classification approach yields an overall average accuracy equal to 85.70% over the 36 datasets.

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Chen, Yi Lan, and Huan Bao Wang. "A Combinatorial Classifier for Error-Data in Joining Processes with Diverse-Granular Computing." Advanced Materials Research 548 (July 2012): 740–43. http://dx.doi.org/10.4028/www.scientific.net/amr.548.740.

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In this paper, we present a novel hybrid classification model with fuzzy clustering and design a newly combinatorial classifier for error-data in joining processes with diverse-granular computing, which is an ensemble of a naïve Bayes classifier with fuzzy c-means clustering. And we apply it to improve classification performance of traditional hard classifiers in more complex real-world situations. The fuzzy c-means clustering is applied to a fuzzy partition based on a given propositional function to augment the combinatorial classifier. This strategy would work better than a conventional hard classifier without fuzzy clustering. Proper scale granularity of objects contributes to higher classification performance of the combinatorial classifier. Our experimental results show the newly combinatorial classifier has improved the accuracy and stability of classification.

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Sharma, Sarang, Sheifali Gupta, Deepali Gupta, Ayman Altameem, Abdul Khader Jilani Saudagar, Ramesh Chandra Poonia, and Soumya Ranjan Nayak. "HTLML: Hybrid AI Based Model for Detection of Alzheimer’s Disease." Diagnostics 12, no. 8 (July 29, 2022): 1833. http://dx.doi.org/10.3390/diagnostics12081833.

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Alzheimer’s disease (AD) is a degenerative condition of the brain that affects the memory and reasoning abilities of patients. Memory is steadily wiped out by this condition, which gradually affects the brain’s ability to think, recall, and form intentions. In order to properly identify this disease, a variety of manual imaging modalities including CT, MRI, PET, etc. are being used. These methods, however, are time-consuming and troublesome in the context of early diagnostics. This is why deep learning models have been devised that are less time-intensive, require less high-tech hardware or human interaction, continue to improve in performance, and are useful for the prediction of AD, which can also be verified by experimental results obtained by doctors in medical institutions or health care facilities. In this paper, we propose a hybrid-based AI-based model that includes the combination of both transfer learning (TL) and permutation-based machine learning (ML) voting classifier in terms of two basic phases. In the first phase of implementation, it comprises two TL-based models: namely, DenseNet-121 and Densenet-201 for features extraction, whereas in the second phase of implementation, it carries out three different ML classifiers like SVM, Naïve base and XGBoost for classification purposes. The final classifier outcomes are evaluated by means of permutations of the voting mechanism. The proposed model achieved accuracy of 91.75%, specificity of 96.5%, and an F1-score of 90.25. The dataset used for training was obtained from Kaggle and contains 6200 photos, including 896 images classified as mildly demented, 64 images classified as moderately demented, 3200 images classified as non-demented, and 1966 images classified as extremely mildly demented. The results show that the suggested model outperforms current state-of-the-art models. These models could be used to generate therapeutically viable methods for detecting AD in MRI images based on these results for clinical prospective.

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Shah, Syed Mohsin Ali, Syed Muhammad Usman, Shehzad Khalid, Ikram Ur Rehman, Aamir Anwar, Saddam Hussain, Syed Sajid Ullah, Hela Elmannai, Abeer D. Algarni, and Waleed Manzoor. "An Ensemble Model for Consumer Emotion Prediction Using EEG Signals for Neuromarketing Applications." Sensors 22, no. 24 (December 12, 2022): 9744. http://dx.doi.org/10.3390/s22249744.

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Traditional advertising techniques seek to govern the consumer’s opinion toward a product, which may not reflect their actual behavior at the time of purchase. It is probable that advertisers misjudge consumer behavior because predicted opinions do not always correspond to consumers’ actual purchase behaviors. Neuromarketing is the new paradigm of understanding customer buyer behavior and decision making, as well as the prediction of their gestures for product utilization through an unconscious process. Existing methods do not focus on effective preprocessing and classification techniques of electroencephalogram (EEG) signals, so in this study, an effective method for preprocessing and classification of EEG signals is proposed. The proposed method involves effective preprocessing of EEG signals by removing noise and a synthetic minority oversampling technique (SMOTE) to deal with the class imbalance problem. The dataset employed in this study is a publicly available neuromarketing dataset. Automated features were extracted by using a long short-term memory network (LSTM) and then concatenated with handcrafted features like power spectral density (PSD) and discrete wavelet transform (DWT) to create a complete feature set. The classification was done by using the proposed hybrid classifier that optimizes the weights of two machine learning classifiers and one deep learning classifier and classifies the data between like and dislike. The machine learning classifiers include the support vector machine (SVM), random forest (RF), and deep learning classifier (DNN). The proposed hybrid model outperforms other classifiers like RF, SVM, and DNN and achieves an accuracy of 96.89%. In the proposed method, accuracy, sensitivity, specificity, precision, and F1 score were computed to evaluate and compare the proposed method with recent state-of-the-art methods.

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