On quick attribute reduction in decision-theoretic rough set models

doi:10.1016/j.ins.2015.09.057

	On quick attribute reduction in decision-theoretic rough set models
	Meng, Zuqiang 1; Shi, Zhongzhi 2
	2016-02-10
发表期刊	INFORMATION SCIENCES
ISSN	0020-0255
卷号	330 页码:226-244
摘要	Compared with the Pawlak rough set model, derision-theoretic rough set (DTRS) models have relatively powerful data processing capability for complex and low quality data. However, the non-monotonicity of the criteria used in DTRS models causes many problems, particularly the inefficiency of attribute reduction algorithms, which greatly narrows their applications. Few systematic studies have been reported on how to construct efficient reduction methods for DTRS models. This paper discusses this problem and constructs an efficient reduction algorithm for DTRS models incorporating three aspects: taking advantage of common characteristics of data sets and employing division techniques, a fast division approach to computing equivalence classes is proposed, which is the most frequently used and the most time-consuming basic operation in the reduction algorithm; extracting a "monotonic ingredient" from decision systems, an effective heuristic function is constructed for the reduction algorithm, which can guide searches for the algorithm and has better ability to find shorter super-reducts; a novel algorithm is proposed to search the power set space of a given super-reduct, which has a shorter average search length than the existing backtracking methods. Finally, the reduction algorithm is proposed that combines these improvements. Experimental results demonstrate that these improvements are effective and the proposed reduction algorithm is relatively efficient. (C) 2015 Elsevier Inc. All rights reserved.
关键词	Decision-theoretic rough set model Attribute reduction Efficiency Rough set theory Equivalence class Super-reduct
DOI	10.1016/j.ins.2015.09.057
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61363027] ; National Basic Research Programme of China[2013CB329502] ; Natural Science Foundation of Guangxi Province, China[2015GXNSFAA139292]
WOS研究方向	Computer Science
WOS类目	Computer Science, Information Systems
WOS记录号	WOS:000367485300014
出版者	ELSEVIER SCIENCE INC
引用统计	被引频次：23[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/2XEOYT63/8984
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Meng, Zuqiang
作者单位	1.Guangxi Univ, Coll Comp Elect & Informat, Nanning 530004, Peoples R China 2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
推荐引用方式 GB/T 7714	Meng, Zuqiang,Shi, Zhongzhi. On quick attribute reduction in decision-theoretic rough set models[J]. INFORMATION SCIENCES,2016,330:226-244.
APA	Meng, Zuqiang,&Shi, Zhongzhi.(2016).On quick attribute reduction in decision-theoretic rough set models.INFORMATION SCIENCES,330,226-244.
MLA	Meng, Zuqiang,et al."On quick attribute reduction in decision-theoretic rough set models".INFORMATION SCIENCES 330(2016):226-244.