Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2002.1004431
Title: Mining multiple comprehensible classification rules using genetic programming
Authors: Tan, K.C. 
Tay, A. 
Lee, T.H. 
Heng, C.M.
Issue Date: 2002
Citation: Tan, K.C., Tay, A., Lee, T.H., Heng, C.M. (2002). Mining multiple comprehensible classification rules using genetic programming. Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002 2 : 1302-1307. ScholarBank@NUS Repository. https://doi.org/10.1109/CEC.2002.1004431
Abstract: Genetic programming (GP) has emerged as a promising approach to deal with the classification task in data mining. This paper extends the tree representation of GP to evolve multiple comprehensible IF-THEN classification rules. We introduce a concept mapping technique for the 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 on nine benchmark data sets, 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. © 2002 IEEE.
Source Title: Proceedings of the 2002 Congress on Evolutionary Computation, CEC 2002
URI: http://scholarbank.nus.edu.sg/handle/10635/129919
DOI: 10.1109/CEC.2002.1004431
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