Please use this identifier to cite or link to this item:
|Title:||A distributed evolutionary classifier for knowledge discovery in data mining|
|Authors:||Tan, K.C. |
Evolutionary algorithms (EA)
|Citation:||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|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Feb 14, 2019
WEB OF SCIENCETM
checked on Feb 5, 2019
checked on Feb 2, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.