Open AccessAttribute Reduction Based on the Fuzzy Rough Set with Lukasiewicz Implication Operator
Author(s) -
Yuanhong Ma,
Yu Zhong,
Zou Jie-tao
Publication year - 2022
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/2224/1/012063
Subject(s) - rough set , operator (biology) , reduction (mathematics) , mathematics , closure (psychology) , simple (philosophy) , fuzzy set , field (mathematics) , similarity (geometry) , closure operator , set (abstract data type) , fuzzy logic , data mining , selection (genetic algorithm) , algorithm , computer science , artificial intelligence , algebra over a field , discrete mathematics , closed set , pure mathematics , image (mathematics) , philosophy , repressor , chemistry , biochemistry , geometry , epistemology , transcription factor , market economy , programming language , economics , gene
Attribute reduction is a common technique and has made breakthroughs in many aspects. One of the major development directions of attribute reduction is the extended fuzzy rough set models, which is embodied in the selection of fuzzy similarity relations and operators, eventually the derived membership functions. In view of the relatively simple selection of implication operators in related research, this article discusses the impact of different implication operators in the fuzzy rough sets. Secondly, the rough set model that relies on Lukasiewicz implication operator is further improved, and the proof of the closure of the new operator on the positive field is given. Finally, a new algorithm is given, and experiments are designed to prove the feasibility of the new algorithm based on eight public data sets.