Optimal Rule Set Generation Using Pso Algorithm

Classification and Prediction is an important research area of data mining. Construction of classifier model for any decision system is an important job for many data mining applications. The objective of developing such a classifier is to classify unlabeled dataset into classes. Here we have applied a discrete Particle Swarm Optimization (PSO) algorithm for selecting optimal classification rule sets from huge number of rules possibly exist in a dataset. In the proposed DPSO algorithm, decision matrix approach was used for generation of initial possible classification rules from a dataset. Then the proposed algorithm discovers important or significant rules from all possible classification rules without sacrificing predictive accuracy. The proposed algorithm deals with discrete valued data, and its initial population of candidate solutions contains particles of different sizes. The experiment has been done on the task of optimal rule selection in the data sets collected from UCI repository. Experimental results show that the proposed algorithm can automatically evolve on average the small number of conditions per rule and a few rules per rule set, and achieved better classification performance of predictive accuracy for few classes.