Abstract
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.
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Imam, T., Tickle, K. (2010). Class Information Adapted Kernel for Support Vector Machine. In: Wong, K.W., Mendis, B.S.U., Bouzerdoum, A. (eds) Neural Information Processing. Models and Applications. ICONIP 2010. Lecture Notes in Computer Science, vol 6444. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-17534-3_15
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DOI: https://doi.org/10.1007/978-3-642-17534-3_15
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-17533-6
Online ISBN: 978-3-642-17534-3
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