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A novel classifier ensemble method with sparsity and diversity
Yin, Xu-Cheng1; Huang, Kaizhu2; Hao, Hong-Wei3; Iqbal, Khalid1; Wang, Zhi-Bin4
Source PublicationNEUROCOMPUTING
AbstractWe consider the classifier ensemble problem in this paper. Due to its superior performance to individual classifiers, class ensemble has been intensively studied in the literature. Generally speaking, there are two prevalent research directions on this, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. While most current approaches are emphasized on either sparsity or diversity only, we investigate the classifier ensemble by learning both sparsity and diversity simultaneously. We manage to formulate the classifier ensemble problem with the sparsity or/and diversity learning in a general framework. In particular, the classifier ensemble with sparsity and diversity can be represented as a mathematical optimization problem. We then propose a heuristic algorithm, capable of obtaining ensemble classifiers with consideration of both sparsity and diversity. We exploit the genetic algorithm, and optimize sparsity and diversity for classifier selection and combination heuristically and iteratively. As one major contribution, we introduce the concept of the diversity contribution ability so as to select proper classifier components and evolve classifier weights eventually. Finally, we compare our proposed novel method with other conventional classifier ensemble methods such as Bagging, least squares combination, sparsity learning, and AdaBoost, extensively on UCI benchmark data sets and the Pascal Large Scale Learning Challenge 2008 webspam data. The experimental results confirm that our approach leads to better performance in many aspects. (C) 2014 Elsevier B.V. All rights reserved.
KeywordClassifier Ensemble Sparsity Learning Diversity Learning Neural Network Ensembles Genetic Algorithm
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000335486000028
Citation statistics
Cited Times:24[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Affiliation1.Univ Sci & Technol Beijing, Sch Comp & Commun Engn, Dept Comp Sci & Technol, Beijing 100083, Peoples R China
2.Xian Jiaotong Liverpool Univ, Dept Elect & Elect Engn, Suzhou 215123, Peoples R China
3.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
4.China Natl Engn Res Ctr Informat Technol Agr, Beijing 100097, Peoples R China
Recommended Citation
GB/T 7714
Yin, Xu-Cheng,Huang, Kaizhu,Hao, Hong-Wei,et al. A novel classifier ensemble method with sparsity and diversity[J]. NEUROCOMPUTING,2014,134:214-221.
APA Yin, Xu-Cheng,Huang, Kaizhu,Hao, Hong-Wei,Iqbal, Khalid,&Wang, Zhi-Bin.(2014).A novel classifier ensemble method with sparsity and diversity.NEUROCOMPUTING,134,214-221.
MLA Yin, Xu-Cheng,et al."A novel classifier ensemble method with sparsity and diversity".NEUROCOMPUTING 134(2014):214-221.
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