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Journal articles on the topic 'Ensemble Based Classification'

Author: Grafiati

Published: 4 June 2021

Last updated: 1 February 2022

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1

Gui, Wenli, Liping Jing, Liu Yang, and Jian Yu. "Unsupervised Cross-Language Classification with Stratified Sampling-Based Cluster Ensemble." International Journal of Machine Learning and Computing 5, no. 3 (2015): 165–71. http://dx.doi.org/10.7763/ijmlc.2015.v5.502.

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Jurek, Anna, Yaxin Bi, Shengli Wu, and Chris Nugent. "A survey of commonly used ensemble-based classification techniques." Knowledge Engineering Review 29, no. 5 (2013): 551–81. http://dx.doi.org/10.1017/s0269888913000155.

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AbstractThe combination of multiple classifiers, commonly referred to as a classifier ensemble, has previously demonstrated the ability to improve classification accuracy in many application domains. As a result this area has attracted significant amount of research in recent years. The aim of this paper has therefore been to provide a state of the art review of the most well-known ensemble techniques with the main focus on bagging, boosting and stacking and to trace the recent attempts, which have been made to improve their performance. Within this paper, we present and compare an updated vie

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Kilimci, Zeynep H., and Selim Akyokus. "Deep Learning- and Word Embedding-Based Heterogeneous Classifier Ensembles for Text Classification." Complexity 2018 (October 9, 2018): 1–10. http://dx.doi.org/10.1155/2018/7130146.

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The use of ensemble learning, deep learning, and effective document representation methods is currently some of the most common trends to improve the overall accuracy of a text classification/categorization system. Ensemble learning is an approach to raise the overall accuracy of a classification system by utilizing multiple classifiers. Deep learning-based methods provide better results in many applications when compared with the other conventional machine learning algorithms. Word embeddings enable representation of words learned from a corpus as vectors that provide a mapping of words with

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Wang, Bo, Yu Kai Yao, Xiao Ping Wang, and Xiao Yun Chen. "PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM." Applied Mechanics and Materials 701-702 (December 2014): 58–62. http://dx.doi.org/10.4028/www.scientific.net/amm.701-702.58.

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As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier. This paper proposes a new ensemble algorithm based on SVM. The proposed classification algorithm PB-SVM Ensemble consists of some SVM classifiers produced by PCAenSVM and fifty classifiers trained using Bagging, the results are combined to make the final decision on testing set using majority voting

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Alsawalqah, Hamad, Neveen Hijazi, Mohammed Eshtay, et al. "Software Defect Prediction Using Heterogeneous Ensemble Classification Based on Segmented Patterns." Applied Sciences 10, no. 5 (2020): 1745. http://dx.doi.org/10.3390/app10051745.

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Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its ow

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KO, ALBERT HUNG-REN, ROBERT SABOURIN, and ALCEU DE SOUZA BRITTO. "COMPOUND DIVERSITY FUNCTIONS FOR ENSEMBLE SELECTION." International Journal of Pattern Recognition and Artificial Intelligence 23, no. 04 (2009): 659–86. http://dx.doi.org/10.1142/s021800140900734x.

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An effective way to improve a classification method's performance is to create ensembles of classifiers. Two elements are believed to be important in constructing an ensemble: (a) the performance of each individual classifier; and (b) diversity among the classifiers. Nevertheless, most works based on diversity suggest that there exists only weak correlation between classifier performance and ensemble accuracy. We propose compound diversity functions which combine the diversities with the performance of each individual classifier, and show that there is a strong correlation between the proposed

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Alizadeh Moghaddam, S. H., M. Mokhtarzade, and S. A. Alizadeh Moghaddam. "A NEW MULTIPLE CLASSIFIER SYSTEM BASED ON A PSO ALGORITHM FOR THE CLASSIFICATION OF HYPERSPECTRAL IMAGES." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-4/W18 (October 18, 2019): 71–75. http://dx.doi.org/10.5194/isprs-archives-xlii-4-w18-71-2019.

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Abstract. Multiple classifier systems (MCSs) have shown great performance for the classification of hyperspectral images. The requirements for a successful MCS are 1) diversity between ensembles and 2) good classification accuracy of each ensemble. In this paper, we develop a new MCS method based on a particle swarm optimization (PSO) algorithm. Firstly, in each ensemble of the proposed method, called PSO-MCS, PSO identifies a subset of the spectral bands with a high J2 value, which is a measure of class-separability. Then, an SVM classifier is used to classify the input image, applying the se

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Hu, Ruihan, Songbin Zhou, Yisen Liu, and Zhiri Tang. "Margin-Based Pareto Ensemble Pruning: An Ensemble Pruning Algorithm That Learns to Search Optimized Ensembles." Computational Intelligence and Neuroscience 2019 (June 3, 2019): 1–12. http://dx.doi.org/10.1155/2019/7560872.

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The ensemble pruning system is an effective machine learning framework that combines several learners as experts to classify a test set. Generally, ensemble pruning systems aim to define a region of competence based on the validation set to select the most competent ensembles from the ensemble pool with respect to the test set. However, the size of the ensemble pool is usually fixed, and the performance of an ensemble pool heavily depends on the definition of the region of competence. In this paper, a dynamic pruning framework called margin-based Pareto ensemble pruning is proposed for ensembl

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Onan, Aytug. "Hybrid supervised clustering based ensemble scheme for text classification." Kybernetes 46, no. 2 (2017): 330–48. http://dx.doi.org/10.1108/k-10-2016-0300.

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Purpose The immense quantity of available unstructured text documents serve as one of the largest source of information. Text classification can be an essential task for many purposes in information retrieval, such as document organization, text filtering and sentiment analysis. Ensemble learning has been extensively studied to construct efficient text classification schemes with higher predictive performance and generalization ability. The purpose of this paper is to provide diversity among the classification algorithms of ensemble, which is a key issue in the ensemble design. Design/methodol

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10

Ku Abd. Rahim, Ku, I. Elamvazuthi, Lila Izhar, and Genci Capi. "Classification of Human Daily Activities Using Ensemble Methods Based on Smartphone Inertial Sensors." Sensors 18, no. 12 (2018): 4132. http://dx.doi.org/10.3390/s18124132.

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Increasing interest in analyzing human gait using various wearable sensors, which is known as Human Activity Recognition (HAR), can be found in recent research. Sensors such as accelerometers and gyroscopes are widely used in HAR. Recently, high interest has been shown in the use of wearable sensors in numerous applications such as rehabilitation, computer games, animation, filmmaking, and biomechanics. In this paper, classification of human daily activities using Ensemble Methods based on data acquired from smartphone inertial sensors involving about 30 subjects with six different activities

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Yıldırım, Pelin, Ulaş K. Birant, and Derya Birant. "EBOC: Ensemble-Based Ordinal Classification in Transportation." Journal of Advanced Transportation 2019 (March 24, 2019): 1–17. http://dx.doi.org/10.1155/2019/7482138.

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Learning the latent patterns of historical data in an efficient way to model the behaviour of a system is a major need for making right decisions. For this purpose, machine learning solution has already begun its promising marks in transportation as well as in many areas such as marketing, finance, education, and health. However, many classification algorithms in the literature assume that the target attribute values in the datasets are unordered, so they lose inherent order between the class values. To overcome the problem, this study proposes a novel ensemble-based ordinal classification (EB

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De Bock, Koen W., Kristof Coussement, and Dirk Van den Poel. "Ensemble classification based on generalized additive models." Computational Statistics & Data Analysis 54, no. 6 (2010): 1535–46. http://dx.doi.org/10.1016/j.csda.2009.12.013.

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13

Duan, Rui, Sabah Mohammed, and Jinan Fiaidhi. "Ensemble Methods for ECG-Based Heartbeat Classification." International Journal of Control and Automation 12, no. 4 (2019): 29–46. http://dx.doi.org/10.33832/ijca.2019.12.4.03.

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14

Rathor, Sandeep, and R. S. Jadon. "Acoustic domain classification and recognition through ensemble based multilevel classification." Journal of Ambient Intelligence and Humanized Computing 10, no. 9 (2018): 3617–27. http://dx.doi.org/10.1007/s12652-018-1087-6.

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15

Osareh, Alireza, and Bita Shadgar. "An Efficient Ensemble Learning Method for Gene Microarray Classification." BioMed Research International 2013 (2013): 1–10. http://dx.doi.org/10.1155/2013/478410.

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The gene microarray analysis and classification have demonstrated an effective way for the effective diagnosis of diseases and cancers. However, it has been also revealed that the basic classification techniques have intrinsic drawbacks in achieving accurate gene classification and cancer diagnosis. On the other hand, classifier ensembles have received increasing attention in various applications. Here, we address the gene classification issue using RotBoost ensemble methodology. This method is a combination of Rotation Forest and AdaBoost techniques which in turn preserve both desirable featu

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Alzami, Farrikh, Aries Jehan Tamamy, Ricardus Anggi Pramunendar, and Zaenal Arifin. "FUSION OF BAGGING BASED ENSEMBLE FRAMEWORK FOR EPILEPTIC SEIZURE CLASSIFICATION." Transmisi 22, no. 3 (2020): 102–6. http://dx.doi.org/10.14710/transmisi.22.3.102-106.

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The ensemble learning approach, especially in classification, has been widely carried out and is successful in many scopes, but unfortunately not many ensemble approaches are used for the detection and classification of epilepsy in biomedical terms. Compared to using a simple bagging ensemble framework, we propose a fusion bagging-based ensemble framework (FBEF) that uses 3 weak learners in each oracle, using fusion rules, a weak learner will give results as predictors of the oracle. All oracle predictors will be included in the trust factor to get a better prediction and classification. Compa

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17

Wei, Yan Yan, and Tao Sheng Li. "An Empirical Study on Feature Subsampling-Based Ensembles." Applied Mechanics and Materials 239-240 (December 2012): 848–52. http://dx.doi.org/10.4028/www.scientific.net/amm.239-240.848.

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Feature subsampling techniques help to create diverse for classifiers ensemble. In this article we investigate two feature subsampling-base ensemble methods - Random Subspace Method (RSM) and Rotation Forest Method (RFM) to explore their usability with different learning algorithms and the robust on noise data. The experiments show that RSM with IBK work better than RFM and AdaBoost, and RFM with tree classifier and rule classifier achieve prominent improvement than others. We also find that Logistic algorithm is not suitable for any of the three ensembles. When adding classification noise int

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18

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

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19

ZHANG, BAILING. "RELIABLE IMAGE CLASSIFICATION BY COMBINING FEATURES AND RANDOM SUBSPACE SUPPORT VECTOR MACHINE ENSEMBLE." International Journal of Pattern Recognition and Artificial Intelligence 28, no. 03 (2014): 1450005. http://dx.doi.org/10.1142/s0218001414500050.

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We investigate the implementation of image categorization algorithms with a reject option, as a mean to enhance the system reliability and to attain a higher classification accuracy. A reject option is desired in many image-classification applications for which the system should abstain from making decisions on the most uncertain images. Based on the random subspace (RS) ensemble learning model, a highly reliable image classification scheme is proposed by applying RS support vector machine (SVM) ensemble. Being different to previous classifier ensembles which focus on increasing classification

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20

Zhang, Peiming. "Ensemble Classification Restricted Boltzmann Machines: A Deep Learning Based Classification Method." Journal of Information and Computational Science 12, no. 14 (2015): 5299–307. http://dx.doi.org/10.12733/jics20106538.

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21

Kumar, Gulshan, and Krishan Kumar. "The Use of Artificial-Intelligence-Based Ensembles for Intrusion Detection: A Review." Applied Computational Intelligence and Soft Computing 2012 (2012): 1–20. http://dx.doi.org/10.1155/2012/850160.

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In supervised learning-based classification, ensembles have been successfully employed to different application domains. In the literature, many researchers have proposed different ensembles by considering different combination methods, training datasets, base classifiers, and many other factors. Artificial-intelligence-(AI-) based techniques play prominent role in development of ensemble for intrusion detection (ID) and have many benefits over other techniques. However, there is no comprehensive review of ensembles in general and AI-based ensembles for ID to examine and understand their curre

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22

Gu, Zheng Gang, and Kun Hong Liu. "Microarray Data Classification Based on Evolutionary Multiple Classifier System." Applied Mechanics and Materials 130-134 (October 2011): 2077–80. http://dx.doi.org/10.4028/www.scientific.net/amm.130-134.2077.

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Designing an evolutionary multiple classifier system (MCS) is a relatively new research area. In this paper, we propose a genetic algorithm (GA) based MCS for microarray data classification. We construct a feature poll with different feature selection methods first, and then a multi-objective GA is applied to implement ensemble feature selection process so as to generate a set of classifiers. When this GA stops, a set of base classifiers are generated. Here we use all the nondominated individuals in last generation to build an ensemble system and test the proposed ensemble method and the metho

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Choi, Do-Yeon, Kwang-Mo Jeong, and Dong Hoon Lim. "Breast Cancer Classification using Deep Learning-based Ensemble." Journal of Health Informatics and Statistics 43, no. 2 (2018): 140–47. http://dx.doi.org/10.21032/jhis.2018.43.2.140.

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Yu, Guo-xian, Guo-ji Zhang, Jia Wei, and Ya-zhou Ren. "A Multi Graphs Based Transductive Ensemble Classification Method." Journal of Electronics & Information Technology 33, no. 8 (2011): 1883–88. http://dx.doi.org/10.3724/sp.j.1146.2010.01424.

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25

WANG, Xinyue, and Liping JING. "Stratified sampling based ensemble classification for imbalanced data." Journal of Shenzhen University Science and Engineering 36, no. 1 (2019): 24. http://dx.doi.org/10.3724/sp.j.1249.2019.01024.

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Anshary, Muhammad Adi Khairul, and Bambang Riyanto Trilaksono. "Tweet-based Target Market Classification Using Ensemble Method." Journal of ICT Research and Applications 10, no. 2 (2016): 123–39. http://dx.doi.org/10.5614/itbj.ict.res.appl.2016.10.2.3.

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Alıguliyev, Ramiz M., and Makrufa Sh Hajirahimova. "Classification Ensemble Based Anomaly Detection in Network Traffic." Review of Computer Engineering Research 6, no. 1 (2019): 12–23. http://dx.doi.org/10.18488/journal.76.2019.61.12.23.

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Markatopoulou, Fotini, Grigorios Tsoumakas, and Ioannis Vlahavas. "Dynamic ensemble pruning based on multi-label classification." Neurocomputing 150 (February 2015): 501–12. http://dx.doi.org/10.1016/j.neucom.2014.07.063.

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Thenmozhi, K., and M. Rajesh Babu. "Classification of skin disease using ensemble-based classifier." International Journal of Biomedical Engineering and Technology 28, no. 4 (2018): 377. http://dx.doi.org/10.1504/ijbet.2018.095985.

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Rajesh Babu, M., and K. Thenmozhi. "Classification of skin disease using ensemble-based classifier." International Journal of Biomedical Engineering and Technology 28, no. 4 (2018): 377. http://dx.doi.org/10.1504/ijbet.2018.10017204.

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Yu, Kai, Lihong Wang, and Yanwei Yu. "Ordering-Based Kalman Filter Selective Ensemble for Classification." IEEE Access 8 (2020): 9715–27. http://dx.doi.org/10.1109/access.2020.2964849.

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SINGH, Sinam Ajitkumar, and Swanirbhar MAJUMDER. "Short unsegmented PCG classification based on ensemble classifier." TURKISH JOURNAL OF ELECTRICAL ENGINEERING & COMPUTER SCIENCES 28, no. 2 (2020): 875–89. http://dx.doi.org/10.3906/elk-1905-165.

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33

Soares, Rodrigo G. F., Huanhuan Chen, and Xin Yao. "A Cluster-Based Semisupervised Ensemble for Multiclass Classification." IEEE Transactions on Emerging Topics in Computational Intelligence 1, no. 6 (2017): 408–20. http://dx.doi.org/10.1109/tetci.2017.2743219.

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Wu, Hao. "Solder joint defect classification based on ensemble learning." Soldering & Surface Mount Technology 29, no. 3 (2017): 164–70. http://dx.doi.org/10.1108/ssmt-08-2016-0016.

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Purpose This paper aims to inspect the defects of solder joints of printed circuit board in real-time production line, simple computing and high accuracy are primary consideration factors for feature extraction and classification algorithm. Design/methodology/approach In this study, the author presents an ensemble method for the classification of solder joint defects. The new method is based on extracting the color and geometry features after solder image acquisition and using decision trees to guarantee the algorithm’s running executive efficiency. To improve algorithm accuracy, the author pr

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Ahmed, Mahreen, Asma Ghulam Rasool, Hammad Afzal, and Imran Siddiqi. "Improving handwriting based gender classification using ensemble classifiers." Expert Systems with Applications 85 (November 2017): 158–68. http://dx.doi.org/10.1016/j.eswa.2017.05.033.

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36

Efendi, Emre, and Berkan Dulek. "Online EM-Based Ensemble Classification With Correlated Agents." IEEE Signal Processing Letters 28 (2021): 294–98. http://dx.doi.org/10.1109/lsp.2021.3052135.

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Xu, Jian, and Yuqing Zhai. "A Toxic Comment Classification Model Based on Ensemble." Journal of Physics: Conference Series 1873, no. 1 (2021): 012080. http://dx.doi.org/10.1088/1742-6596/1873/1/012080.

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38

Thammasiri, Dech, and Phayung Meesad. "Ensemble Data Classification based on Diversity of Classifiers Optimized by Genetic Algorithm." Advanced Materials Research 433-440 (January 2012): 6572–78. http://dx.doi.org/10.4028/www.scientific.net/amr.433-440.6572.

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In this research we propose an ensemble classification technique base on creating classification from a variety of techniques such as decision trees, support vector machines, neural networks and then choosing optimize the appropriate classifiers by genetic algorithm and also combined by a majority vote in order to increase classification accuracy. From classification accuracy test on Australian Credit, German Credit and Bankruptcy Data, we found that the proposed ensemble classification models selected by genetic algorithm yields highest performance and our algorithms are effective in building

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Thammasiri, Dech, and Phayung Meesad. "Adaboost Ensemble Data Classification Based on Diversity of Classifiers." Advanced Materials Research 403-408 (November 2011): 3682–87. http://dx.doi.org/10.4028/www.scientific.net/amr.403-408.3682.

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In this research we propose an ensemble classification technique based on decision tree, artificial neural network, and support vector machine models weighting classifier by adaboost in order to increase classification accuracy. we used a total of 30 classifiers. The technique generated random data used Bootstrap. Testing Diabites Data from UCI, classification accuracy tests on Diabites Data found that the proposed ensemble classification models weighting classifier by Adaboost yields better performance than that of a single model with the same type of classifier. The result as follows, Diabit

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Mikryukov, A. A., A. V. Babash, and V. A. Sizov. "Classifcation of events in information security systems based on neural networks." Open Education 23, no. 1 (2019): 57–63. http://dx.doi.org/10.21686/1818-4243-2019-1-57-63.

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Purpose of the research.The aim of the study is to increase the effectiveness of information security and to enhance accuracy and promptness of the classification of security events, security incidents, and threats in information security systems. To respond to this challenge, neural network technologies were suggested as a classification tool for information security systems. These technologies allow accommodating incomplete, inaccurate and unidentified raw data, as well as utilizing previously accumulated information on security issues. To address the problem more effectively, collective met

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Venugopal, K. R., D. R. Sowmya, and P. Deepa Shenoy. "Post classification change detection based on feature-based ensemble classifiers." International Journal of Spatio-Temporal Data Science 1, no. 2 (2021): 149. http://dx.doi.org/10.1504/ijstds.2021.10040051.

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Sowmya, D. R., P. Deepa Shenoy, and K. R. Venugopal. "Post classification change detection based on feature-based ensemble classifiers." International Journal of Spatio-Temporal Data Science 1, no. 2 (2021): 149. http://dx.doi.org/10.1504/ijstds.2021.116958.

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Shadman Roodposhti, Majid, Arko Lucieer, Asim Anees, and Brett Bryan. "A Robust Rule-Based Ensemble Framework Using Mean-Shift Segmentation for Hyperspectral Image Classification." Remote Sensing 11, no. 17 (2019): 2057. http://dx.doi.org/10.3390/rs11172057.

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This paper assesses the performance of DoTRules—a dictionary of trusted rules—as a supervised rule-based ensemble framework based on the mean-shift segmentation for hyperspectral image classification. The proposed ensemble framework consists of multiple rule sets with rules constructed based on different class frequencies and sequences of occurrences. Shannon entropy was derived for assessing the uncertainty of every rule and the subsequent filtering of unreliable rules. DoTRules is not only a transparent approach for image classification but also a tool to map rule uncertainty, where rule unc

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Chaudhary, Poonam, and Rashmi Agrawal. "Sensory motor imagery EEG classification based on non-dyadic wavelets using dynamic weighted majority ensemble classification." Intelligent Decision Technologies 15, no. 1 (2021): 33–43. http://dx.doi.org/10.3233/idt-200005.

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The classification accuracy has become a significant challenge and an important task in sensory motor imagery (SMI) electroencephalogram (EEG) based Brain Computer interface (BCI) system. This paper compares ensemble classification framework with individual classifiers. The main objective is to reduce the inference of non-stationary and transient information and improves the classification decision in BCI system. The framework comprises the three phases as follows: (1) the EEG signal first decomposes into triadic frequency bands: low pass band, band pass filter and high pass filter to localize

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Abuassba, Adnan O. M., Dezheng Zhang, Xiong Luo, Ahmad Shaheryar, and Hazrat Ali. "Improving Classification Performance through an Advanced Ensemble Based Heterogeneous Extreme Learning Machines." Computational Intelligence and Neuroscience 2017 (2017): 1–11. http://dx.doi.org/10.1155/2017/3405463.

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Extreme Learning Machine (ELM) is a fast-learning algorithm for a single-hidden layer feedforward neural network (SLFN). It often has good generalization performance. However, there are chances that it might overfit the training data due to having more hidden nodes than needed. To address the generalization performance, we use a heterogeneous ensemble approach. We propose an Advanced ELM Ensemble (AELME) for classification, which includes Regularized-ELM, L2-norm-optimized ELM (ELML2), and Kernel-ELM. The ensemble is constructed by training a randomly chosen ELM classifier on a subset of train

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Chen, Wen, Xinyu Li, Liang Gao, and Weiming Shen. "Improving Computer-Aided Cervical Cells Classification Using Transfer Learning Based Snapshot Ensemble." Applied Sciences 10, no. 20 (2020): 7292. http://dx.doi.org/10.3390/app10207292.

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Cervical cells classification is a crucial component of computer-aided cervical cancer detection. Fine-grained classification is of great clinical importance when guiding clinical decisions on the diagnoses and treatment, which remains very challenging. Recently, convolutional neural networks (CNN) provide a novel way to classify cervical cells by using automatically learned features. Although the ensemble of CNN models can increase model diversity and potentially boost the classification accuracy, it is a multi-step process, as several CNN models need to be trained respectively and then be se

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Alhayali, Royida A. Ibrahem, Munef Abdullah Ahmed, Yasmin Makki Mohialden, and Ahmed H. Ali. "Efficient method for breast cancer classification based on ensemble hoffeding tree and naïve Bayes." Indonesian Journal of Electrical Engineering and Computer Science 18, no. 2 (2020): 1074. http://dx.doi.org/10.11591/ijeecs.v18.i2.pp1074-1080.

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<p><span>The most dangerous type of cancer suffered by women above 35 years of age is breast cancer. Breast Cancer datasets are normally characterized by missing data, high dimensionality, non-normal distribution, class imbalance, noisy, and inconsistency. Classification is a machine learning (ML) process which has a significant role in the prediction of outcomes, and one of the outstanding supervised classification methods in data mining is Naives Bayess Classification (NBC). Naïve Bayes Classifications is good at predicting outcomes and often outperforms other classifications tec

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Saifan, Ahmad A., and Lina Abu-wardih. "Software Defect Prediction Based on Feature Subset Selection and Ensemble Classification." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 14, no. 2 (2020): 213–28. http://dx.doi.org/10.37936/ecti-cit.2020142.224489.

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Two primary issues have emerged in the machine learning and data mining community: how to deal with imbalanced data and how to choose appropriate features. These are of particular concern in the software engineering domain, and more specifically the field of software defect prediction. This research highlights a procedure which includes a feature selection technique to single out relevant attributes, and an ensemble technique to handle the class-imbalance issue. In order to determine the advantages of feature selection and ensemble methods we look at two potential scenarios: (1) Ensemble model

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Naz, Mehreen, Kashif Zafar, and Ayesha Khan. "Ensemble Based Classification of Sentiments Using Forest Optimization Algorithm." Data 4, no. 2 (2019): 76. http://dx.doi.org/10.3390/data4020076.

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Feature subset selection is a process to choose a set of relevant features from a high dimensionality dataset to improve the performance of classifiers. The meaningful words extracted from data forms a set of features for sentiment analysis. Many evolutionary algorithms, like the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), have been applied to feature subset selection problem and computational performance can still be improved. This research presents a solution to feature subset selection problem for classification of sentiments using ensemble-based classifiers. It consists o

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Guedj, Benjamin, and Bhargav Srinivasa Desikan. "Kernel-Based Ensemble Learning in Python." Information 11, no. 2 (2020): 63. http://dx.doi.org/10.3390/info11020063.

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We propose a new supervised learning algorithm for classification and regression problems where two or more preliminary predictors are available. We introduce KernelCobra, a non-linear learning strategy for combining an arbitrary number of initial predictors. KernelCobra builds on the COBRA algorithm introduced by Biau et al. (2016), which combined estimators based on a notion of proximity of predictions on the training data. While the COBRA algorithm used a binary threshold to declare which training data were close and to be used, we generalise this idea by using a kernel to better encapsulat

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