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2006

Using discriminant analysis for multi-class classification: an experimental investigation

9 years 11 months ago
Using discriminant analysis for multi-class classification: an experimental investigation
Abstract. Many supervised machine learning tasks can be cast as multi-class classification problems. Support vector machines (SVMs) excel at binary classification problems, but the elegant theory behind large-margin hyperplane cannot be easily extended to their multi-class counterparts. On the other hand, it was shown that the decision hyperplanes for binary classification obtained by SVMs are equivalent to the solutions obtained by Fisher's linear discriminant on the set of support vectors. Discriminant analysis approaches are well known to learn discriminative feature transformations in the statistical pattern recognition literature and can be easily extend to multi-class cases. The use of discriminant analysis, however, has not been fully experimented in the data mining literature. In this paper, we explore the use of discriminant analysis for multi-class classification problems. We evaluate the performance of discriminant analysis on a large collection of benchmark datasets an...
Tao Li, Shenghuo Zhu, Mitsunori Ogihara
Added 13 Dec 2010
Updated 13 Dec 2010
Type Journal
Year 2006
Where KAIS
Authors Tao Li, Shenghuo Zhu, Mitsunori Ogihara
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