Finding minimal rough set reducts with particle swarm optimization

Authors:
Xiangyang Wang;Jie Yang;Ningsong Peng;Xiaolong Teng
Affiliations:
Inst. of Image Processing & Pattern Recognition, Jiaotong University, Shanghai, P.R. China;Inst. of Image Processing & Pattern Recognition, Jiaotong University, Shanghai, P.R. China;Inst. of Image Processing & Pattern Recognition, Jiaotong University, Shanghai, P.R. China;Inst. of Image Processing & Pattern Recognition, Jiaotong University, Shanghai, P.R. China
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part I
Year:
2005

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Abstract

We propose a new algorithm to find minimal rough set reducts by using Particle Swarm Optimization (PSO). Like Genetic Algorithm, PSO is also a type of evolutionary algorithm. But compared with GA, PSO does not need complex operators as crossover and mutation that GA does, it requires only primitive and simple mathematical operators, and is computationally inexpensive in terms of both memory and times. The experiments on some UCI data compare our algorithm with GA-based, and other deterministic rough set reduction algorithms. The results show that PSO is efficient to minimal rough set reduction.