Multi-class contour preserving classification [Full Text] ( Piyabute Fuangkhon Thitipong Tanprasert 2018-04-17T07:57:56Z 2018-04-17T07:57:56Z 2012-08
dc.description.abstract The original contour preserving classification technique was proposed to improve the robustness and weight fault tolerance of a neu- ral network applied with a two-class linearly separable problem. It was recently found to be improving the level of accuracy of two-class classi- fication. This paper presents an augmentation of the original technique to improve the level of accuracy of multi-class classification by better preservation of the shape or distribution model of a multi-class problem. The test results on six real world multi-class datasets from UCI ma- chine learning repository present that the proposed technique supports multi-class data and can improve the level of accuracy of multi-class classification more effectively.
dc.format.extent 8 pages en_US
dc.format.mimetype application/pdf en_US
dc.identifier.citation International Conference on Intelligent Data Engineering and Automated Learning (IDEAL’12), Natal, Brazil, 29-31 August 2012 Lecture Notes in Computer Science LNCS 7435, Springer Berlin / Heidelberg pp. 35-42, ISSN: 0302-9743 (print), ISBN: 978-3-642-32638-7, doi:10.1007/978-3-642-32639-4_5. en_US
dc.language.iso eng en_US
dc.rights.holder Piyabuth Fuangkhon en_US
dc.rights.holder Thitipong Tanprasert en_US
dc.subject Contour preserving classification
dc.subject Data preprocessor
dc.subject Neural networks (Computer science)
dc.subject Outpost vector
dc.subject Pattern classification
dc.title Multi-class contour preserving classification en_US
dc.type Text en_US
mods.genre Conference Paper en_US
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