Fuzzy multi-category proximal support vector classification via generalized eigenvalues

doi:10.1007/s00500-006-0130-2

2006-08-10

PRE

Jayadeva

Khemchandani, Reshma

Chandra, Suresh *

DOI：10.1007/s00500-006-0130-2

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摘要

En 中文

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.

Keyword:

support vector machines

generalized eigenvalues

proximal classifier

multi-category classification

fuzzy data classification