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dc.contributor.author Kempitiya, T
dc.contributor.editor Perera, I
dc.contributor.editor Meedeniya, D
dc.contributor.editor Perera, S
dc.date.accessioned 2022-12-12T05:17:36Z
dc.date.available 2022-12-12T05:17:36Z
dc.date.issued 2014-09
dc.identifier.citation ************ en_US
dc.identifier.uri http://dl.lib.uom.lk/handle/123/19751
dc.description.abstract K nearest neighbour classification (KNN) is a popular non parametric and lazy algorithm for classification. gaKnn framework is a implementation of the KNN algorithm combine with genetic algorithm. It provides genetic algorithm optimization for KNN algorithm which will optimize the weight values for each attribute and k value. In this paper, I proposed improvements for the current implementation of the gaKnn framework to improve its usability and performance using kd tree to improve the KNN algorithm, different data and file type usage and regression algorithm based on k nearest neighbour. Mainly it introduce three modules for the current implementation of the gaKnn framework namely csv file reader and writer module, large dataset module and KNN regression module. en_US
dc.language.iso en en_US
dc.publisher Department of Computer Science and Engineering, University of Moratuwa. en_US
dc.subject K nearest neighbour en_US
dc.subject Regression en_US
dc.subject Kd tree en_US
dc.title Improving the usability of gaknn framework en_US
dc.type Conference-Full-text en_US
dc.identifier.faculty Engineering en_US
dc.identifier.department Department of Computer Science and Engineering en_US
dc.identifier.year 2014 en_US
dc.identifier.conference Proceedings of the CSE Symposium 2014 en_US
dc.identifier.place Moratuwa, Sri Lanka. en_US
dc.identifier.pgnos pp. 29-32 en_US
dc.identifier.proceeding Proceedings of the CSE Symposium 2014 en_US
dc.identifier.email thimal.10@cse.mrt.ac.lk en_US


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