Abstract: 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 systemlike memory vector is utilized to produce multiple rules as well as to remove redundant rules. The proposed GP classifier is wlidated 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.
A GP classifier has been proposed in REF , using an artificial immune system-like memory vector to generate multiple comprehensible classification rules.
Mining multiple comprehensible classification rules using genetic programming
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
Proceedings of the 2002 Congress on Evolutionary Computation. CEC'02 (Cat. No.02TH8600)
Computer Science