Multi-class contour preserving classification
Multi-class contour preserving classification
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2012-08
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eng
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application/pdf
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8 pages
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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.
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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.