A Dual-Objective Evolutionary Algorithm for Rules Extraction in Data Mining
Computational Optimization and Applications
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Expert Systems with Applications: An International Journal
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Intelligent Decision Technologies
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IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics - Special issue on game theory
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This paper presents a distributed coevolutionary classifier (DCC) for extracting comprehensible rules in data mining. It allows different species to be evolved cooperatively and simultaneously, while the computational workload is shared among multiple computers over the Internet. Through the intercommunications among different species of rules and rule sets in a distributed manner, the concurrent processing and computational speed of the coevolutionary classifiers are enhanced. The advantage and performance of the proposed DCC are validated upon various datasets obtained from the UCI machine learning repository. It is shown that the predicting accuracy of DCC is robust and the computation time is reduced as the number of remote engines increases. Comparison results illustrate that the DCC produces good classification rules for the datasets, which are competitive as compared to existing classifiers in literature.