Reducing the storage requirements of 1-v-1 support vector machine multi-classifiers

Authors:
Pawan Lingras;Cory J. Butz
Affiliations:
Department of Math and Computer Science, Saint Mary's University, Halifax, Nova Scotia, Canada;Department of Computer Science, University of Regina, Regina, Saskatchewan, Canada
Venue:
RSFDGrC'05 Proceedings of the 10th international conference on Rough Sets, Fuzzy Sets, Data Mining, and Granular Computing - Volume Part II
Year:
2005

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Abstract

The methods for extending binary support vectors machines (SVMs) can be broadly divided into two categories, namely, 1-v-r (one versus rest) and 1-v-1 (one versus one). The 1-v-r approach tends to have higher training time, while 1-v-1 approaches tend to create a large number of binary classifiers that need to be analyzed and stored during the operational phase. This paper describes how rough set theory may help in reducing the storage requirements of the 1-v-1 approach in the operational phase.