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|Title:||Class Decomposition for GA-Based Classifier Agents - A Pitt Approach||Authors:||Guan, S.-U.
Incremental genetic algorithm
|Issue Date:||Feb-2004||Citation:||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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