Abstract
Given a dataset, where each point is labeled with one of M labels, we propose a technique for multi-category proximal support vector classification via generalized eigenvalues (MGEPSVMs). Unlike Support Vector Machines that classify points by assigning them to one of M disjoint half-spaces, here points are classified by assigning them to the closest of M non-parallel planes that are close to their respective classes. When the data contains samples belonging to several classes, classes often overlap, and classifiers that solve for several non-parallel planes may often be able to better resolve test samples. In multicategory classification tasks, a training point may have similarities with prototypes of more than one class. This information can be used in a fuzzy setting. We propose a fuzzy multi-category classifier that utilizes information about the membership of training samples, to improve the generalization ability of the classifier. The desired classifier is obtained by using one-from-rest (OFR) separation for each class, i.e. 1: M -1 classification. Experimental results demonstrate the efficacy of the proposed classifier over MGEPSVMs.
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Jayadeva, Khemchandani, R. & Chandra, S. Fuzzy multi-category proximal support vector classification via generalized eigenvalues. Soft Comput 11, 679–685 (2007). https://doi.org/10.1007/s00500-006-0130-2
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DOI: https://doi.org/10.1007/s00500-006-0130-2