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dc.contributor.authorSaptoro, Agus
dc.contributor.authorTade, Moses
dc.contributor.authorVuthaluru, Hari
dc.date.accessioned2017-01-30T15:18:31Z
dc.date.available2017-01-30T15:18:31Z
dc.date.created2015-03-03T20:16:34Z
dc.date.issued2012
dc.identifier.citationSaptoro, A. and Tade, M. and Vuthaluru, H. 2012. A Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models. Chemical Product and Process Modeling. 7 (1): pp. 1-14.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/45101
dc.identifier.doi10.1515/1934-2659.1645
dc.description.abstract

This paper proposes a method, namely MDKS (Kennard-Stone algorithm based on Mahalanobis distance), to divide the data into training and testing subsets for developing artificial neural network (ANN) models. This method is a modified version of the Kennard-Stone (KS) algorithm. With this method, better data splitting, in terms of data representation and enhanced performance of developed ANN models, can be achieved. Compared with standard KS algorithm and another improved KS algorithm (data division based on joint x - y distances (SPXY) method), the proposed method has also shown a better performance. Therefore, the proposed technique can be used as an advantageous alternative to other existing methods of data splitting for developing ANN models. Care should be taken when dealing with large amount of dataset since they may increase the computational load for MDKS due to its variance-covariance matrix calculations.

dc.publisherThe Berkeley Electronic Press
dc.subjectdata division
dc.subjectkennard-stone algorithm
dc.subjectANN models
dc.subjectMDKS
dc.titleA Modified Kennard-Stone Algorithm for Optimal Division of Data for Developing Artificial Neural Network Models
dc.typeJournal Article
dcterms.source.volume7
dcterms.source.number1
dcterms.source.startPage1
dcterms.source.endPage16
dcterms.source.issn19342659
dcterms.source.titleChemical Product and Process Modeling
curtin.departmentCurtin Sarawak
curtin.accessStatusOpen access


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