Decision region distribution preservation reduction in decision-theoretic rough set model
文献类型：期刊论文
| 作者 | Ma, Xi'ao1,2; Wang, Guoyin3; Yu, Hong2; Li, Tianrui1 |
| 刊名 | INFORMATION SCIENCES
 |
| 出版日期 | 2014-09-10 |
| 卷号 | 278页码:614-640 |
| 关键词 | Decision-theoretic rough set model Conditional information entropy Attribute reduction Probabilistic rough set model Decision region distribution preservation reduct |
| ISSN号 | 0020-0255 |
| DOI | 10.1016/j.ins.2014.03.078 |
| 通讯作者 | Wang, GY (reprint author), Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Inst Elect Informat Technol, Chongqing 400714, Peoples R China. |
| 英文摘要 | In the Pawlak rough set model, the positive region, the boundary region and the non-negative region are monotonic with respect to the set inclusion of attributes. However, the monotonicity property of the decision regions (positive region, boundary region or non-negative region) with respect to the set inclusion of attributes does not hold in the decision-theoretic rough set model. Therefore, the decision regions may be changed after attribute reduction based on quantitative preservation or qualitative preservation of decision regions. This effect is observed partly because three decision regions are defined by introducing the probabilistic threshold values. In addition, heuristic reduction algorithms based on decision regions may find super reducts because of the non-monotonicity of decision regions. To address the above issues, this paper proposes solutions to the attribute reduction problem based on decision region preservation in the decision-theoretic rough set model. First, the (alpha, beta) positive region distribution preservation reduct, the (alpha, beta) boundary region distribution preservation reduct and the (alpha, beta) negative region distribution preservation reduct are introduced into the decision-theoretic rough set model. Second, three new monotonic measures are constructed by considering variants of the conditional information entropy, from which we can obtain the heuristic reduction algorithms. The results of the experimental analysis validate the monotonicity of new measures and verify the effectiveness of decision region distribution preservation reducts. (C) 2014 Elsevier Inc. All rights reserved. |
| 资助项目 | Natural Science Foundation of China[61272060] ; Natural Science Foundation of China[61379114] ; Key Natural Science Foundation of Chongqing of China[CSTC2013jjB40003] |
| WOS研究方向 | Computer Science |
| 语种 | 英语 |
| WOS记录号 | WOS:000338390100042 |
| 出版者 | ELSEVIER SCIENCE INC |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/1368]  |
| 专题 | 中国科学院重庆绿色智能技术研究院 |
| 通讯作者 | Wang, Guoyin |
| 作者单位 | 1.Southwest Jiaotong Univ, Sch Informat Sci & Technol, Chengdu 610031, Peoples R China 2.Chongqing Univ Posts & Telecommun, Chongqing Key Lab Computat Intelligence, Chongqing 400065, Peoples R China 3.Chinese Acad Sci, Chongqing Inst Green & Intelligent Technol, Inst Elect Informat Technol, Chongqing 400714, Peoples R China |
| 推荐引用方式 GB/T 7714 | Ma, Xi'ao,Wang, Guoyin,Yu, Hong,et al. Decision region distribution preservation reduction in decision-theoretic rough set model[J]. INFORMATION SCIENCES,2014,278:614-640. |
| APA | Ma, Xi'ao,Wang, Guoyin,Yu, Hong,&Li, Tianrui.(2014).Decision region distribution preservation reduction in decision-theoretic rough set model.INFORMATION SCIENCES,278,614-640. |
| MLA | Ma, Xi'ao,et al."Decision region distribution preservation reduction in decision-theoretic rough set model".INFORMATION SCIENCES 278(2014):614-640. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
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