Scaling up the hybrid Particle Swarm Optimization algorithm for nominal data-sets

Abstract

Recently, the hybrid Particle Swarm Optimisation/Ant Colony Optimisation (PSO/ACO) has been proposed for discovery of classification rules. An improved version of this hybrid scheme, PSO/ACO2 algorithm, can directly cope with nominal attributes without converting them into numerical ones. Although PSO/ACO2 can handle nominal values, it suffers from high computational complexity for large datasets. Beside variety of classification methods which exist to provide more compact set of rules, this study propose an approach which reduces the computational complexity of PSO/ACO2 in order to make it suitable for classification of large datasets. This work is developed the K-mode as a method of sampling from the datasets. In this regard a modification is employed to this algorithm in order to decrease the computational time as well as maintaining the accuracy of the algorithm. Further contribution of this paper is utilizing a new fitness measure for the algorithm. This measure has a robust theoretical background. The combination of the proposed modified K-mode method and the introduced fitness measure led to speed the obtained results up. The experimental result shows the efficiency of the proposed algorithm in comparison with its competitors.