A Genetic Algorithm-Based Approach for Classification Rule Discovery

Data mining has as goal to extract knowledge from large databases. To extract this knowledge, a database may be considered as a large search space, and a mining algorithm as a search strategy. In general, a search space consists of an enormous number of elements, which make it is infeasible to search exhaustively. As a search strategy, genetic algorithms have been applied successfully in many fields. In this paper, we present a genetic algorithm-based approach for mining classification rules from large database. For emphasizing on predictive accuracy, comprehensibility and interestingness of the rules and simplifying the implementation of a genetic algorithm, we discuss detail the design of encoding, genetic operator and fitness function of genetic algorithm for this task. Experimental result shows that genetic algorithm proposed in this paper is suitable for classification rule mining and those rules discovered by the algorithm have higher classification performance to unknown data.