DocumentCode :
1634818
Title :
Mining multiple comprehensible classification rules using genetic programming
Author :
Tan, K.C. ; Tay, A. ; Lee, T.H. ; Heng, C.M.
Author_Institution :
Dept. of Electr. & Comput. Eng., Nat. Univ. of Singapore, Singapore
Volume :
2
fYear :
2002
fDate :
6/24/1905 12:00:00 AM
Firstpage :
1302
Lastpage :
1307
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
Keywords :
data mining; genetic algorithms; pattern classification; programming; redundancy; IF-THEN rule evolution; application domains; artificial immune system-like memory vector; benchmark data sets; concept mapping technique; covering algorithm; data mining; fitness evaluation; genetic programming; multiple comprehensible classification rules; redundant rule removal; simulation; tree representation; Arithmetic; Classification tree analysis; Data mining; Databases; Evolutionary computation; Genetic mutations; Genetic programming; Mathematics; Testing; Tree data structures;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on
Conference_Location :
Honolulu, HI
Print_ISBN :
0-7803-7282-4
Type :
conf
DOI :
10.1109/CEC.2002.1004431
Filename :
1004431
Link To Document :
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