A class decomposition approach for GA-based classifiers

Sheng Uei Guan*, Fangming Zhu

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

4 Citations (Scopus)


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.

Original languageEnglish
Pages (from-to)271-278
Number of pages8
JournalEngineering Applications of Artificial Intelligence
Issue number3
Publication statusPublished - Apr 2005
Externally publishedYes


  • Class decomposition
  • Classification
  • Genetic algorithm

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