Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCB.2003.817030
Title: Class Decomposition for GA-Based Classifier Agents - A Pitt Approach
Authors: Guan, S.-U. 
Zhu, F.
Keywords: Class decomposition
Classifier agents
Genetic algorithm
Incremental genetic algorithm
Issue Date: Feb-2004
Source: Guan, S.-U., Zhu, F. (2004-02). Class Decomposition for GA-Based Classifier Agents - A Pitt Approach. IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics 34 (1) : 381-392. ScholarBank@NUS Repository. https://doi.org/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.
Source Title: IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
URI: http://scholarbank.nus.edu.sg/handle/10635/55296
ISSN: 10834419
DOI: 10.1109/TSMCB.2003.817030
Appears in Collections:Staff Publications

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