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

Thang Truong Nguyen, Nguyen Long Giang, Dai Thanh Tran, Trung Tuan Nguyen, Huy Quang Nguyen, Anh Viet Pham and Thi Duc Vu
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Thang Truong Nguyen: Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
Nguyen Long Giang: Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
Dai Thanh Tran: University of Economic and Technical Industries, Vietnam
Trung Tuan Nguyen: National Economics University, Vietnam
Huy Quang Nguyen: Institute of Information Technology, Vietnam Academy of Science and Technology, Vietnam
Anh Viet Pham: Hanoi University of Industry, Vietnam
Thi Duc Vu: Information Technology Institute, Vietnam National University, Vietnam

International Journal of Data Warehousing and Mining (IJDWM), 2021, vol. 17, issue 4, 67-100

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.

Date: 2021
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