Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

doi:10.1016/S1005-8885(16)60065-1

中国邮电高校学报(英文) ›› 2016, Vol. 23 ›› Issue (6): 16-23.doi: 10.1016/S1005-8885(16)60065-1

• Artificial Intelligence • 上一篇下一篇

Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

徐凯,张清华,薛玉斌,胡峰

重庆邮电大学

收稿日期:2016-08-31 修回日期:2016-12-21 出版日期:2016-12-31 发布日期:2016-12-30
通讯作者: 张清华 E-mail:zhangqh@cqupt.edu.cn
基金资助:
面向领域用户知识发现的数据结构化建模与多粒度融合;大数据中的多粒度知识发现模型与方法研究

Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

Received:2016-08-31 Revised:2016-12-21 Online:2016-12-31 Published:2016-12-30

摘要/Abstract

摘要： Rough set theory is an important tool to solve uncertain problems. Attribute reduction, as one of the core issues of rough set theory, has been proven to be an effective method for knowledge acquisition. Most of heuristic attribute reduction algorithms usually keep the positive region of a target set unchanged and ignore boundary region information. So, how to acquire knowledge from the boundary region of a target set in a multi-granulation space is an interesting issue. In this paper, a new concept, fuzziness of an approximation set of rough set is put forward firstly. Then the change rules of fuzziness in changing granularity spaces are analyzed. Finally, a new algorithm for attribute reduction based on the fuzziness of 0.5-approximation set is presented. Several experimental results show that the attribute reduction by the proposed method has relative better classification characteristics compared with various classification algorithms.

关键词: rough set, approximation set, fuzziness, attribute reduction, multi-granulation

Abstract: Rough set theory is an important tool to solve uncertain problems. Attribute reduction, as one of the core issues of rough set theory, has been proven to be an effective method for knowledge acquisition. Most of heuristic attribute reduction algorithms usually keep the positive region of a target set unchanged and ignore boundary region information. So, how to acquire knowledge from the boundary region of a target set in a multi-granulation space is an interesting issue. In this paper, a new concept, fuzziness of an approximation set of rough set is put forward firstly. Then the change rules of fuzziness in changing granularity spaces are analyzed. Finally, a new algorithm for attribute reduction based on the fuzziness of 0.5-approximation set is presented. Several experimental results show that the attribute reduction by the proposed method has relative better classification characteristics compared with various classification algorithms.

Key words: rough set, approximation set, fuzziness, attribute reduction, multi-granulation

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Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

Attribute reduction based on fuzziness of approximation set in multi-granulation spaces

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