Please use this identifier to cite or link to this item: https://doi.org/10.1109/TSMCC.2004.841911
Title: A distributed evolutionary classifier for knowledge discovery in data mining
Authors: Tan, K.C. 
Yu, Q.
Lee, T.H. 
Keywords: Classification
Coevolution
Data mining
Evolutionary algorithms (EA)
Issue Date: May-2005
Source: Tan, K.C., Yu, Q., Lee, T.H. (2005-05). A distributed evolutionary classifier for knowledge discovery in data mining. IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews 35 (2) : 131-142. ScholarBank@NUS Repository. https://doi.org/10.1109/TSMCC.2004.841911
Abstract: 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. © 2005 IEEE.
Source Title: IEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
URI: http://scholarbank.nus.edu.sg/handle/10635/54082
ISSN: 10946977
DOI: 10.1109/TSMCC.2004.841911
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