A hybrid approach to design efficient learning classifiers

Recently, use of a Learning Classifier System (LCS) has become promising method for performing classification tasks and data mining. For the task of classification, the quality of the rule set is usually evaluated as a whole rather than evaluating the quality of a single rule. The present investigation proposes a hybrid of the C4.5 rule induction algorithm and a GA (Genetic Algorithm) approach to extract an accuracy based rule set. At the initial stage, C4.5 is applied to a classification problem to generate a rule set. Then, the GA is used to refine the rules learned. Using eight well-known data sets, it has been shown that the present work, in comparison to C4.5 alone and UCS, provides a marked improvement in a number of cases.