Opposition-Based Discrete PSO Using Natural Encoding for Classification Rule Discovery

Abstract

In this paper we present a new Discrete Particle Swarm Optimization approach to induce rules from discrete data. The proposed algorithm, called Opposition-based Natural Discrete PSO (ONDPSO), initializes its population by taking into account the discrete nature of the data. Particles are encoded using a Natural Encoding scheme. Each member of the population updates its position iteratively on the basis of a newly designed position update rule. Opposition-based learning is implemented in the optimization process. The encoding scheme and position update rule used by the algorithm allows individual terms corresponding to different attributes within the rule's antecedent to be a disjunction of the values of those attributes. The performance of the proposed algorithm is evaluated against seven different datasets using a tenfold testing scheme. The achieved median accuracy is compared against various evolutionary and non-evolutionary classification techniques. The algorithm produces promising results by creating highly accurate and precise rules for each dataset.