A class decomposition approach for GA-based classifiers

Authors:
Sheng-Uei Guan;Fangming Zhu
Affiliations:
Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore;Department of Electrical and Computer Engineering, National University of Singapore, 10 Kent Ridge Crescent, Singapore 119260, Singapore
Venue:
Engineering Applications of Artificial Intelligence
Year:
2005

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Abstract

Genetic algorithm (GA) has been used as a conventional method for classifiers to evolve solutions adaptively for classification problems. In this paper, a new approach using class decomposition is proposed to improve the performance of GA-based classifiers. A classification problem is fully partitioned 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 the results obtained are integrated and evolved further for a final solution. A scheme based on Fisher's linear discriminant (FLD) computation is used to estimate the difficulty of separating two classes. Based on the FLD information derived, different integration approaches are proposed and their performance is compared. The experiment results on a benchmark data set show that class decomposition can achieve higher classification rate than the normal GA and FLD-based integration improves the classification accuracy further.