%0 Journal Article
	%A María-Dolores Cubiles-de-la-Vega and  Rafael Pino-Mejías and  Esther-Lydia Silva-Ramírez
	%D 2012
	%J International Journal of Electrical and Computer Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 64, 2012
	%T Clustering Multivariate Empiric Characteristic Functions for Multi-Class SVM Classification
	%U https://publications.waset.org/pdf/15061
	%V 64
	%X A dissimilarity measure between the empiric
characteristic functions of the subsamples associated to the different
classes in a multivariate data set is proposed. This measure can be
efficiently computed, and it depends on all the cases of each class. It
may be used to find groups of similar classes, which could be joined
for further analysis, or it could be employed to perform an
agglomerative hierarchical cluster analysis of the set of classes. The
final tree can serve to build a family of binary classification models,
offering an alternative approach to the multi-class SVM problem. We
have tested this dendrogram based SVM approach with the oneagainst-
one SVM approach over four publicly available data sets,
three of them being microarray data. Both performances have been
found equivalent, but the first solution requires a smaller number of
binary SVM models.
	%P 380 - 384