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On quick attribute reduction in decision-theoretic rough set models

文献类型：期刊论文

作者Meng, Zuqiang1; Shi, Zhongzhi2
刊名INFORMATION SCIENCES
出版日期2016-02-10
卷号330页码:226-244
关键词Decision-theoretic rough set model Attribute reduction Efficiency Rough set theory Equivalence class Super-reduct
ISSN号0020-0255
DOI10.1016/j.ins.2015.09.057
英文摘要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.
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
语种英语
WOS记录号WOS:000367485300014
出版者ELSEVIER SCIENCE INC
源URL[http://119.78.100.204/handle/2XEOYT63/8983]  
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.

入库方式： OAI收割

来源：计算技术研究所

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