Performance Analysis of Hybrid Swarm Intelligence Rule Induction Algorithm

Data mining is used to extract potential information from data base. Rule induction is usedto extract information from data base and display it in IF-THEN rule format. First the classificationalgorithm builds a predictive model from the training data set and then measure the accuracy of themodel by using test data set.This work proposes a hybrid rule induction algorithm using CooperativeParticle Swarm (PSO) with Tabu search (TS), and Ant Colony Optimization (ACO). Real world database consist of both nominal and continuous attributes. ACO based classification algorithms performwell in nominal data base. PSO based classification algorithms perform well in continuous data basewhere it converts nominal attributes into numerical values. In conventional PSO, there is no guaranteefor local optimal solution. So, the proposed algorithm use tabu search in PSO to improve the searchcapability and integrate pheromone concept of ACO to handle real world classification problems. Ituses cooperative concurrent PSO model to implement the algorithm and run two tasks simultaneously inparallel machines. The output of the work compares with the existing algorithm performance in severalpublic domain data sets. The comparison results provide a evidence that: (a) The proposed algorithm iscompetitive with existing algorithm with respect to predictive accuracy; and the rule lists discovered bythe algorithm are considerably simpler (smaller) than those discovered by the existing algorithm and (b) Reduce the execution time of the algorithm