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- W2170339732 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." @default.
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- W2170339732 date "2004-02-01" @default.
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- W2170339732 title "Class Decomposition for GA-Based Classifier Agents—A Pitt Approach" @default.
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- W2170339732 doi "https://doi.org/10.1109/tsmcb.2003.817030" @default.
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