A direct ensemble classifier for imbalanced multiclass learning
Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as on...
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my.uum.repo.123212014-10-21T01:21:36Z http://repo.uum.edu.my/12321/ A direct ensemble classifier for imbalanced multiclass learning Sainin, Mohd Shamrie Alfred, Rayner QA76 Computer software Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers.The DECIML framework has been tested with several benchmark dataset and shows promising results. 2012 Conference or Workshop Item PeerReviewed application/pdf en http://repo.uum.edu.my/12321/1/063.pdf Sainin, Mohd Shamrie and Alfred, Rayner (2012) A direct ensemble classifier for imbalanced multiclass learning. In: 4th Conference on Data Mining and Optimization (DMO), 2-4 Sept. 2012, Langkawi. http://dx.doi.org/10.1109/DMO.2012.6329799 doi:10.1109/DMO.2012.6329799 |
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QA76 Computer software Sainin, Mohd Shamrie Alfred, Rayner A direct ensemble classifier for imbalanced multiclass learning |
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Researchers have shown that although traditional direct classifier algorithm can be easily applied to multiclass classification, the performance of a single classifier is decreased with the existence of imbalance data in multiclass classification tasks.Thus, ensemble of classifiers has emerged as one of the hot topics in multiclass classification tasks for imbalance problem for data mining and machine learning domain.Ensemble learning is an effective technique that has increasingly been adopted to combine multiple learning algorithms to improve overall prediction accuraciesand may outperform any single sophisticated classifiers.In this paper, an ensemble learner called a Direct Ensemble Classifier for Imbalanced Multiclass Learning (DECIML) that combines simple nearest neighbour and Naive Bayes algorithms is proposed. A combiner method called OR-tree is used to combine the decisions obtained from the ensemble classifiers.The DECIML framework has been tested with several benchmark dataset and shows promising results. |
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Conference or Workshop Item |
author |
Sainin, Mohd Shamrie Alfred, Rayner |
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Sainin, Mohd Shamrie Alfred, Rayner |
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Sainin, Mohd Shamrie |
title |
A direct ensemble classifier for imbalanced multiclass learning |
title_short |
A direct ensemble classifier for imbalanced multiclass learning |
title_full |
A direct ensemble classifier for imbalanced multiclass learning |
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A direct ensemble classifier for imbalanced multiclass learning |
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A direct ensemble classifier for imbalanced multiclass learning |
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direct ensemble classifier for imbalanced multiclass learning |
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2012 |
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http://repo.uum.edu.my/12321/1/063.pdf http://repo.uum.edu.my/12321/ http://dx.doi.org/10.1109/DMO.2012.6329799 |
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