Creating and sharing knowledge for telecommunications

Reject Option Paradigm for the Reduction of Support Vectors

Sousa, R.Sousa ; R. da Rocha Neto, ARRN ; A. Barreto, G.A.B. ; Cardoso, JSCardoso ; Coimbra, M.

Reject Option Paradigm for the Reduction of Support Vectors, Proc European Symp. on Artificial Neural Networks - ESANN , Bruges, Belgium, Vol. -, pp. - - -, April, 2014.

Digital Object Identifier: 0

Download Full text PDF ( 277 KBs)


In this paper we introduce a new conceptualization for the reduction of the number of support vectors (SVs) for an efficient design of support vector machines. The techniques here presented provide a good balance between SVs reduction and generalization capability. Our proposal explores concepts from classification with reject option. These methods output a third class (the rejected instances) for a binary problem when a prediction cannot be given with sufficient confidence. Rejected instances along with misclassified ones are discarded from the original data to give rise to a classification problem that can be linearly solved. Our experimental study on two benchmark datasets show significant gains in terms of SVs reduction with competitive performances.