An approach for adaptive associative classification

doi:10.1016/j.eswa.2011.03.079

	An approach for adaptive associative classification
	Wang, Xiaofeng 1,2; Yue, Kun 3; Niu, WenJia 4; Shi, Zhongzhi 1
	2011-09-01
发表期刊	EXPERT SYSTEMS WITH APPLICATIONS
ISSN	0957-4174
卷号	38 期号:9 页码:11873-11883
摘要	As a branch of classification, associative classification combines the basic ideas of association rule mining and general classification. Previous studies show that associative classification can achieve a higher classification accuracy comparing with traditional classification methods, such as C4.5. It is known that new frequent patterns may emerge from the classified resources during classification, and these newly emerging frequent patterns can be used to build new classification rules. However, this dynamic characteristics in associative classification has not been well reflected in traditional methods. In this paper, we propose an enhanced associative classification method by integrating the dynamic property in the process of associative classification. In the proposed method, we employ co-training to refine the discovered emerging frequent patterns for classification rule extension and utilize the maximum entropy model for class label prediction. The empirical study shows that our method can be used to classify increasing resources efficiently and effectively. Crown Copyright (C) 2011 Published by Elsevier Ltd. All rights reserved.
关键词	Associative classification Frequent pattern mining Co-training Emerging frequent pattern Maximum entropy model
DOI	10.1016/j.eswa.2011.03.079
收录类别	SCI
语种	英语
资助项目	National Basic Research Priorities Programme[2007CB311004] ; National Basic Research Program of China (973 Program)[2011CB302803] ; National S&T Major Project of China[2010ZX03006-006] ; CAS[KGCX2-YW-149] ; National Natural Science Foundation of China[61035003] ; National Natural Science Foundation of China[60933004] ; National Natural Science Foundation of China[61072085] ; National Natural Science Foundation of China[60903141] ; National Natural Science Foundation of China[60970088] ; National Natural Science Foundation of China[61063009]
WOS研究方向	Computer Science ; Engineering ; Operations Research & Management Science
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Operations Research & Management Science
WOS记录号	WOS:000291118500132
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计	被引频次：14[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/12968
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Wang, Xiaofeng
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Grad Univ, Beijing 100190, Peoples R China 3.Yunnan Univ, Sch Informat Sci & Engn, Dept Comp Sci & Engn, Kunming 650091, Peoples R China 4.Chinese Acad Sci, Inst Acoust, High Performance Network Lab, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Wang, Xiaofeng,Yue, Kun,Niu, WenJia,et al. An approach for adaptive associative classification[J]. EXPERT SYSTEMS WITH APPLICATIONS,2011,38(9):11873-11883.
APA	Wang, Xiaofeng,Yue, Kun,Niu, WenJia,&Shi, Zhongzhi.(2011).An approach for adaptive associative classification.EXPERT SYSTEMS WITH APPLICATIONS,38(9),11873-11883.
MLA	Wang, Xiaofeng,et al."An approach for adaptive associative classification".EXPERT SYSTEMS WITH APPLICATIONS 38.9(2011):11873-11883.