Data Mining by Discrete PSO Using Natural Encoding

Abstract

In this paper we have presented a new Discrete Particle Swarm Optimization approach to induce rules from the discrete data. Particles are encoded using Natural Encoding scheme. Encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes in the rule antecedent to be disjunction of values of those attributes. The performance of the proposed algorithm is evaluated against six different datasets using tenfold testing scheme. Achieved error rate has been compared against various evolutionary and non-evolutionary classification techniques. The algorithm produces promising results by creating highly accurate rules for each dataset.