Class information adapted kernel for support vector machine
conference contributionposted on 2017-12-06, 00:00 authored by Tasadduq ImamTasadduq Imam, Kevin TickleKevin Tickle
This article presents a support vector machine (SVM) learning approach that adapts class information within the kernel computation. Experiments on fifteen publicly available datasets are conducted and the impact of proposed approach for varied settings are observed. It is noted that the new approach generally improves minority class prediction, depicting it as a well-suited scheme for imbalanced data. However, a SVM based customization is also developed that significantly improves prediction performance in terms of different measures. Overall, the proposed method holds promise with potential for future extensions.
Parent TitleICONIP 2010 : Neural information processing : theory and algorithms, 17th international conference, proceedings, part II, 20-25 November 2010, Sydney, Australia
Number of Pages8
Place of PublicationHeidelberg, Germany
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