Artificial Bee Colony based Data Mining Algorithms for Classification Tasks

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dc.contributor.author Shukran, Mohd en_US
dc.contributor.author Chung, Yuk en_US
dc.contributor.author Yeh, Wei-Chang en_US
dc.contributor.author Wahid, Noorhaniza en_US
dc.contributor.author Zaidi, Ahmad en_US
dc.contributor.editor en_US
dc.date.accessioned 2012-10-12T03:33:45Z
dc.date.available 2012-10-12T03:33:45Z
dc.date.issued 2011 en_US
dc.identifier 2011002071 en_US
dc.identifier.citation Shukran Mohd et al. 2011, 'Artificial Bee Colony based Data Mining Algorithms for Classification Tasks', Canadian Center of Science and Education, vol. 5, no. 4, pp. 217-231. en_US
dc.identifier.issn 1913-1844 en_US
dc.identifier.other C1 en_US
dc.identifier.uri http://hdl.handle.net/10453/18263
dc.description.abstract Artificial Bee Colony (ABC) algorithm is considered new and widely used in searching for optimum solutions. This is due to its uniqueness in problem-solving method where the solution for a problem emerges from intelligent behaviour of honeybee swarms. This paper proposes the use of the ABC algorithm as a new tool for Data Mining particularly in classification tasks. Moreover, the proposed ABC for Data Mining were implemented and tested against six traditional classification algorithms classifiers. From the obtained results, ABC proved to be a suitable candidate for classification tasks. This can be proved in the experimental result where the performance of the proposed ABC algorithm has been tested by doing the experiments using UCI datasets. The results obtained in these experiments indicate that ABC algorithm are competitive, not only with other evolutionary techniques, but also to industry standard algorithms such as PART, SOM, Naive Bayes, Classification Tree and Nearest Neighbour (kNN), and can be successfully applied to more demanding problem domains. en_US
dc.language en_US
dc.publisher Canadian Center of Science and Education en_US
dc.title Artificial Bee Colony based Data Mining Algorithms for Classification Tasks en_US
dc.parent Modern Applied Science en_US
dc.journal.volume 5 en_US
dc.journal.number 4 en_US
dc.publocation Canadian en_US
dc.identifier.startpage 217 en_US
dc.identifier.endpage 231 en_US
dc.cauo.name FEIT.Faculty of Engineering & Information Technology en_US
dc.conference Verified OK en_US
dc.for 080100 en_US
dc.personcode 0000074095 en_US
dc.personcode 0000065024 en_US
dc.personcode 106463 en_US
dc.personcode 0000074096 en_US
dc.personcode 0000074097 en_US
dc.percentage 100 en_US
dc.classification.name Artificial Intelligence and Image Processing en_US
dc.classification.type FOR-08 en_US
dc.edition en_US
dc.custom en_US
dc.date.activity en_US
dc.location.activity en_US
dc.description.keywords Artificial bee colony algorithm, Data mining, Local search strategy en_US


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