PSO based Swarm Intelligence Technique for Multi- Objective Classification Rule Mining
Anil Kumar · International Journal of Computer Applications · 2016
Today's real world faces different kinds of complex optimization problems.The existing methodologies can't cope of with such complex problems.This paper presents classification rule mining as a multi-objective problem rather than a single objective one.Multi-Objective optimization is a challenging area and focus for research.Here two modern domains of research are discussed one is swarm intelligence and other is data mining.In this paper PSO is taken as taken as a swarm intelligence algorithm and classification rule mining is taken as the problem domain.In classification rule discovery, classifiers are designed through the following two phases: rule extraction and rule selection.In the rule extraction phase, a large number of classification rules are extracted from training data.This phase is based on two rule evaluation criteria: support (coverage) and confidence.An association rule mining technique is used to extract classification rules satisfying pre-specified threshold values of minimum support (coverage) and confidence.In second phase, a small number of rules are targeted from the extracted rules to design an accurate and compact classifier.In this paper, I used PSO for multiple objective rule selection to maximize the accuracy of the rule sets and minimize their complexity.