A Novel Filter-Wrapper Algorithm on Intuitionistic Fuzzy Set for Attribute Reduction From Decision Tables

Author:

Nguyen Thang Truong1ORCID,Giang Nguyen Long1ORCID,Tran Dai Thanh2,Nguyen Trung Tuan3,Nguyen Huy Quang1,Pham Anh Viet4,Vu Thi Duc5

Affiliation:

1. Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam

2. University of Economic and Technical Industries, Vietnam

3. National Economics University, Vietnam

4. Hanoi University of Industry, Vietnam

5. Information Technology Institute, Vietnam National University, Vietnam

Abstract

Attribute reduction from decision tables is one of the crucial topics in data mining. This problem belongs to NP-hard and many approximation algorithms based on the filter or the filter-wrapper approaches have been designed to find the reducts. Intuitionistic fuzzy set (IFS) has been regarded as the effective tool to deal with such the problem by adding two degrees, namely the membership and non-membership for each data element. The separation of attributes in the view of two counterparts as in the IFS set would increase the quality of classification and reduce the reducts. From this motivation, this paper proposes a new filter-wrapper algorithm based on the IFS for attribute reduction from decision tables. The contributions include a new instituitionistics fuzzy distance between partitions accompanied with theoretical analysis. The filter-wrapper algorithm is designed based on that distance with the new stopping condition based on the concept of delta-equality. Experiments are conducted on the benchmark UCI machine learning repository datasets.

Publisher

IGI Global

Subject

Hardware and Architecture,Software

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