A novel classifier ensemble method with sparsity and diversity

CORC > 自动化研究所 > 中国科学院自动化研究所 > 数字内容技术与服务研究中心 > 听觉模型与认知计算

	A novel classifier ensemble method with sparsity and diversity
	Yin, Xu-Cheng; Huang, Kaizhu; Hao, Hong-Wei; Iqbal, Khalid; Wang, Zhi-Bin
刊名	NEUROCOMPUTING
	2014-06-25
卷号	134 页码:214-221
关键词	Classifier Ensemble Sparsity Learning Diversity Learning Neural Network Ensembles Genetic Algorithm
文献子类	Article
英文摘要	We 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.
WOS关键词	NEURAL-NETWORKS ; COMBINING CLASSIFIERS ; COMBINATION ; SELECTION ; RECOGNITION ; CONFIDENCE
WOS研究方向	Computer Science
语种	英语
WOS记录号	WOS:000335486000028
内容类型	期刊论文
源URL	[http://ir.ia.ac.cn/handle/173211/40871]
专题	数字内容技术与服务研究中心_听觉模型与认知计算
推荐引用方式 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.