Artificial Bee Colony based Data Mining Algorithms for Classification Tasks

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dc.contributor.author Shukran, MA
dc.contributor.author Chung, YY
dc.contributor.author Yeh, W
dc.contributor.author Wahid, N
dc.contributor.author Zaidi, AM
dc.date.accessioned 2012-10-12T03:33:45Z
dc.date.issued 2011-01
dc.identifier.citation Modern Applied Science, 2011, 5 (4), pp. 217 - 231
dc.identifier.issn 1913-1844
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.
dc.publisher Canadian Center of Science and Education
dc.relation.isbasedon 10.3844/jcssp.2011.216.224
dc.title Artificial Bee Colony based Data Mining Algorithms for Classification Tasks
dc.type Journal Article
dc.parent Modern Applied Science
dc.journal.volume 4
dc.journal.volume 5
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 0801 Artificial Intelligence and Image Processing
dc.personcode 106463
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
dc.description.keywords Archetype
dc.description.keywords Brand
dc.description.keywords Collective unconscious
dc.description.keywords Consumer
dc.description.keywords Conversation
dc.description.keywords Jung
dc.description.keywords Personal unconscious
dc.description.keywords Primal -------------------------------------------------------------------
pubs.embargo.period Not known
pubs.organisational-group /University of Technology Sydney
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group /University of Technology Sydney/Faculty of Engineering and Information Technology/School of Software
utslib.copyright.status Closed Access
utslib.copyright.date 2015-04-15 12:17:09.805752+10
utslib.collection.history Closed (ID: 3)


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