A Genetic Algorithm Approach using Improved Fitness Function for Classification Rule Mining

Abstract

Classification rule mining from huge amount of data is a challenging issue in data mining. Classification rules describe the relationship between predicting attributes and class label attribute and thus assign class label to unseen predicting attribute values. In this paper, a Genetic algorithm approach with modified fitness function for discovering classification rules has been presented. A flexible encoding scheme for representing a rule, genetic operators like crossover, mutation and also the stated fitness function with confidence, coverage, simplicity and interestingness properties have been exploited for discovering accurate, comprehensible and interesting rules. The results of proposed Genetic algorithm have been compared with existing J48, Jrip, Naive Bayesian algorithms. Experimental results endorse that the proposed algorithm produces relatively less number of classification rules with satisfactory accuracy rates.