A New Rough Set Reduct Algorithm Based on Particle Swarm Optimization

  • Authors:

  • Benxian Yue;Weihong Yao;Ajith Abraham;Hongbo Liu

  • Affiliations:

  • Department of Computer, Dalian University of Technology, Dalian 116023, China and School of Mechanical and Engineeing, Dalian Universityof Technology, Dalian 116023, China;Department of Computer, Dalian University of Technology, Dalian 116023, China;Centre for Quantifiable Quality of Service in Communication Systems, Norwegian University of Science and Technology, Trondheim, Norway and School of Computer Science, Dalian Maritime University, D ...;Department of Computer, Dalian University of Technology, Dalian 116023, China and School of Computer Science, Dalian Maritime University, Dalian 116026, China

  • Venue:
  • IWINAC '07 Proceedings of the 2nd international work-conference on The Interplay Between Natural and Artificial Computation, Part I: Bio-inspired Modeling of Cognitive Tasks
  • Year:
  • 2007

Quantified Score

Hi-index 0.00

Visualization

Abstract

Finding appropriate features is one of the key problems in the increasing applications of rough set theory, which is also one of the bottlenecks of the rough set methodology. Particle Swarm Optimization (PSO) is particularly attractive for this challenging problem. In this paper, we attempt to solve the problem using a particle swarm optimization approach. The proposed approach discover the best feature combinations in an efficient way to observe the change of positive region as the particles proceed through the search space. We evaluate the performance of the proposed PSO algorithm with Genetic Algorithm (GA). Empirical results indicate that the proposed algorithm could be an ideal approach for solving the feature reduction problem when other algorithms failed to give a better solution.