Mining Multiple Comprehensible Classification Rules Using Genetic Programming

Genetic Programming (GP) has been emerged as a promising approach to deal with classification task in data mining. This work extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. In the paper, we introduce a concept mapping technique for fitness evaluation of individuals. A covering algorithm that employs an artificial immune system-like memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is validated upon nine benchmark datasets and the simulation results confirm the viability and effectiveness of the GP approach for solving data mining problems in a wide spectrum of application domains.