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	Integrate classifier diversity evaluation to feature selection based classifier ensemble reduction
	Yao, Gang ; Chao, Fei ; Zeng, Hualin ; Shi, Minghui ; Jiang, Min ; Zhou, Changle ; Chao F(晁飞) ; Ceng HL(曾华琳) ; Shi MH(施明辉) ; Jiang M(江敏) ; Zhou CL(周昌乐)
	2014
关键词	Artificial intelligence
英文摘要	Conference Name:2014 14th UK Workshop on Computational Intelligence, UKCI 2014. Conference Address: Bradford, West Yorkshire, United kingdom. Time:September 8, 2014 - September 10, 2014.; Classifier ensembles improve the performance of single classifier system. However, a classifier ensemble with too many classifiers may occupy a large number of computational time. This paper proposes a new ensemble subset evaluation method that integrates classifier diversity measures into a classifier ensemble reduction framework. The approach is implemented by using three conventional diversity algorithms and one new developed diversity measure method to calculate the diversity's merits within the classifier ensemble reduction framework. The subset evaluation method is demonstrated by the experimental data: the method not only can meet the requirements of high accuracy rate and fewer size, but also its running time is greatly shortened. When the accuracy requirements are not very strict, but the the running time requirements is more stringent, the proposed method is a good choice.
语种	英语
出处	http://dx.doi.org/10.1109/UKCI.2014.6930156
出版者	Institute of Electrical and Electronics Engineers Inc.
内容类型	其他
源URL	[http://dspace.xmu.edu.cn/handle/2288/86888]
专题	信息技术－会议论文
推荐引用方式 GB/T 7714	Yao, Gang,Chao, Fei,Zeng, Hualin,et al. Integrate classifier diversity evaluation to feature selection based classifier ensemble reduction. 2014-01-01.

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