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Attribute reduction with fuzzy rough self-information measures

Citation

Wang, C and Huang, Y and Ding, W and Cao, Z, Attribute reduction with fuzzy rough self-information measures, Information Sciences, 549 pp. 68-86. ISSN 0020-0255 (2021) [Refereed Article]

Copyright Statement

Copyright 2020 Elsevier Inc. All rights reserved.

DOI: doi:10.1016/j.ins.2020.11.021

Abstract

The fuzzy rough set is one of the most effective methods for dealing with the fuzziness and uncertainty of data. However, in most cases this model only considers the information provided by the lower approximation of a decision when it is used to attribute reduction. In a realistic environment, the uncertainty of information is related to lower approximation as well as upper approximation. In this study, we construct four kinds of uncertainty measures by combining fuzzy rough approximations with the concept of self-information. These uncertainty measures can be employed to evaluate the classification ability of attribute subsets. The relationships between these measures are discussed in detail. It is proven that the fourth measure, called relative decision self-information, is better for attribute reduction than the other measures because it considers both the lower and upper approximations of a fuzzy decision. The proposed measures are generalizations of classical measures based on fuzzy rough sets. Finally, we have designed a greedy algorithm for attribute reduction. We validate the effectiveness of the proposed method by comparing the experimental results for efficiency and accuracy with those of three other algorithms using fundamental data.

Item Details

Item Type:Refereed Article
Keywords:fuzzy rough set, self-information, fuzzy rough approximation, attribute reduction, fuzzy rough self-information measures
Research Division:Information and Computing Sciences
Research Group:Artificial intelligence
Research Field:Knowledge representation and reasoning
Objective Division:Information and Communication Services
Objective Group:Information systems, technologies and services
Objective Field:Artificial intelligence
UTAS Author:Cao, Z (Dr Zehong Cao)
ID Code:141889
Year Published:2021
Web of Science® Times Cited:8
Deposited By:Information and Communication Technology
Deposited On:2020-12-01
Last Modified:2021-04-29
Downloads:0

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