A 2-population classifier system

Author

Chen, Yi-Chang ; Shen, Shin-Ren ; Chang, Shan-Lin

Author_Institution

Dept. of Inf. Manage., Nat. Pingtung Inst. of Commerce, Pingtung, Taiwan

Volume

6

fYear

2010

fDate

10-12 Aug. 2010

Firstpage

3093

Lastpage

3097

Abstract

This study proposes a 2-population classifier system to increase the computing efficiency of classifier system. The system is applied to solve the Wisconsin Breast Cancer (WBC) problem. The system is compared to the traditional learning classifier system (LCS) and Wilson´s extend classifier system (XCS) in terms of computing efficiency and prediction accuracy. On the WBC problem, the average execution time for 2-population classifier system is roughly 19.45% of XCS. Meanwhile, the 2-population classifier system is higher than LCS even to XCS according to the accuracy rate comparisons. Thus, this study presents the 2-population classifier system to achieve both higher prediction accuracy ability and lower execution time.

Keywords

cancer; medical computing; pattern classification; 2-population classifier system; WBC problem; Wilson extend classifier system; Wisconsin breast cancer problem; learning classifier system; Accuracy; Artificial intelligence; Binary codes; Classification algorithms; Computer science; Computers; Zero current switching; 2-population classifier system; classifier system; component; soft computing;

fLanguage

English

Publisher

ieee

Conference_Titel

Natural Computation (ICNC), 2010 Sixth International Conference on

Conference_Location

Yantai, Shandong

Print_ISBN

978-1-4244-5958-2

Type

conf

DOI

10.1109/ICNC.2010.5584654

Filename

5584654