Class decomposition for GA-based classifier agents--a Pitt approach

Sheng-Uei Guan; Fangming Zhu

doi:10.1109/tsmcb.2003.817030

Class decomposition for GA-based classifier agents--a Pitt approach

IEEE Trans Syst Man Cybern B Cybern. 2004 Feb;34(1):381-92. doi: 10.1109/tsmcb.2003.817030.

Authors

Sheng-Uei Guan¹, Fangming Zhu

Affiliation

¹ Department of Electrical and Computer Engineering, National University of Singapore, Singapore. eleguans@nus.edu.sg

PMID: 15369080
DOI: 10.1109/tsmcb.2003.817030

Abstract

This paper proposes a class decomposition approach to improve the performance of GA-based classifier agents. This approach partitions a classification problem into several class modules in the output domain, and each module is responsible for solving a fraction of the original problem. These modules are trained in parallel and independently, and results obtained from them are integrated to form the final solution by resolving conflicts. Benchmark classification data sets are used to evaluate the proposed approaches. The experiment results show that class decomposition can help achieve higher classification rate with training time reduced.