Fuzzy Integral Based-Mixture to Speed Up the One-Against-All Multiclass SVMS

The one-against-all (OAA) is the most widely used implementation of multiclass SVM. For a K-class problem, it performs K binary SVMs designed to separate a class from all the others. All SVMs are performed over the full database which is, however, a time-consuming task especially for large scale problems. To overcome this limitation, we propose a mixture scheme to speed-up the training of OAA. Thus, each binary problem is divided into a set of sub-problems trained by different SVM modules whose outputs are subsequently combined throughout a gating network. The proposed mixture scheme is based on Sugeno's fuzzy integral in which the gater is expressed by fuzzy measures. Experiments were conducted on two benchmark databases which concern handwritten digit recognition (ODR) and face recognition (FR). The results indicate that the proposed scheme allows a significant training and testing time improvement. In addition, it can be easily implemented in parallel