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An effective discretization based on Class-Attribute Coherence Maximization

文献类型：期刊论文

作者Li, Min1,3,4; Deng, ShaoBo2,3,4; Feng, Shengzhong1; Fan, Jianping1
刊名PATTERN RECOGNITION LETTERS
出版日期2011-11-01
卷号32期号:15页码:1962-1973
关键词Discretization CAIM CACM Classification Class-Attribute Independence Redundancy (CAIR)
ISSN号0167-8655
DOI10.1016/j.patrec.2011.08.008
英文摘要Discretization of continuous data is one of the important pre-processing tasks in data mining and knowledge discovery. Generally speaking, discretization can lead to improved predictive accuracy of induction algorithms, and the obtained rules are normally shorter and more understandable. In this paper, we present the Class-Attribute Coherence Maximization (CACM) algorithm and the Efficient-CACM algorithm. We have compared the performance of our algorithms with the most relevant discretization algorithm, Fast Class-Attribute Interdependence Maximization (Fast-CAIM) discertization algorithm (Kurgan and Cios, 2003). Empirical evaluation of our algorithms and Fast-CAIM on 12 well-known datasets shows that ours generate the superior discretization scheme, which can significantly improve the classification performance of C4.5 and RBF-SVM classifier. As to the execution time of discretization, ours also prove faster than Fast-CAIM algorithm, with the Efficient-CACM algorithm having the shortest execution time. (C) 2011 Elsevier B.V. All rights reserved.
资助项目National High Technology Research and Development Program of China (863 Programs)[2007AA120502] ; National High Technology Research and Development Program of China (863 Programs)[2006AA01A114] ; Jiangxi Education Department, China[GJJ11632]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000297885900004
出版者ELSEVIER SCIENCE BV
源URL[http://119.78.100.204/handle/2XEOYT63/13047]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Min
作者单位1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen 518055, Guangdong, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100080, Peoples R China
3.Chinese Acad Sci, Grad Sch, Beijing 100080, Peoples R China
4.Nanchang Inst Technol, Nanchang 330099, Jiangxi, Peoples R China
推荐引用方式
GB/T 7714		 Li, Min,Deng, ShaoBo,Feng, Shengzhong,et al. An effective discretization based on Class-Attribute Coherence Maximization[J]. PATTERN RECOGNITION LETTERS,2011,32(15):1962-1973.
APA Li, Min,Deng, ShaoBo,Feng, Shengzhong,&Fan, Jianping.(2011).An effective discretization based on Class-Attribute Coherence Maximization.PATTERN RECOGNITION LETTERS,32(15),1962-1973.
MLA Li, Min,et al."An effective discretization based on Class-Attribute Coherence Maximization".PATTERN RECOGNITION LETTERS 32.15(2011):1962-1973.

入库方式： OAI收割

来源：计算技术研究所

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