Fuzzy multi-category proximal support vector classification via generalized eigenvalues

  • Authors:

  • Jayadeva;Reshma Khemchandani;Suresh Chandra

  • Affiliations:

  • Indian Institute of Technology, Department of Electrical Engineering, 110016, Hauz Khas, New Delhi, India;Indian Institute of Technology, Department of Mathematics, 110016, Hauz Khas, New Delhi, India;Indian Institute of Technology, Department of Mathematics, 110016, Hauz Khas, New Delhi, India

  • Venue:
  • Soft Computing - A Fusion of Foundations, Methodologies and Applications
  • Year:
  • 2007

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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.