Journal of Systems Engineering and Electronics ›› 2012, Vol. 23 ›› Issue (1): 160-166.doi: 10.1109/JSEE.2012.00020


Cooperative extended rough attribute reduction algorithm based on improved PSO

Weiping Ding1,2,*, Jiandong Wang1, and Zhijin Guan2   

  1. 1. College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 210016, P. R. China;
    2. School of Computer Science and Technology, Nantong University, Nantong 226019, P. R. China
  • Online:2012-02-25 Published:2010-01-03


Particle swarm optimization (PSO) is a new heuristic algorithm which has been applied to many optimization problems successfully. Attribute reduction is a key studying point of the rough set theory, and it has been proven that computing minimal reduction of decision tables is a non-derterministic polynomial (NP)-hard problem. A new cooperative extended attribute reduction algorithm named Co-PSAR based on improved PSO is proposed, in which the cooperative evolutionary strategy with suitable fitness functions is involved to learn a good hypothesis for accelerating the optimization of searching minimal attribute reduction. Experiments on Benchmark functions and University of California, Irvine (UCI) data sets, compared with other algorithms, verify the superiority of the Co-PSAR algorithm in terms of the convergence speed, efficiency and accuracy for the attribute reduction.