Optimizing subclass discriminant Error Correcting Output Codes using particle swarm optimization

Abstract

Abstract-Error-Correcting Output Codes (ECOC) reveal a common way to model multi-class classification problems. According to this state of the art technique, a multi-class problem is decomposed into several binary ones. Additionally, on the ECOC framework we can apply the subclass technique (sub-ECOC), where by splitting the initial classes of the problem we create larger but easier to solve ECOC configurations. The multi-class problem's decomposition is achieved via a discriminant tree creation procedure. This discriminant tree's creation is controlled by a triplet of thresholds that define a set of user defined splitting standards. The selection of the thresholds plays a major role in the classification performance. In our work we show that by optimizing these thresholds via particle swarm optimization we improve significantly the classification performance. Moreover, using Support Vector Machines (SVMs) as classifiers we can optimize in the same time both the thresholds of sub-ECOC and the parameters C and σ of the SVMs, resulting in even better classification performance. Extensive experiments in both real and artificial data illustrate the superiority of the proposed approach in terms of performance.

Pruning of Error Correcting Output Codes by optimization of accuracy–diversity trade off

“…Unlike with the methods mentioned above, subclass technique (subECOC) is developed in Bouzas et al (2011) on the ECOC framework where by splitting the initial classes of the problem, larger but easier problem to solve ECOC configurations. The multiclass problem's decomposition is performed by discriminant tree approach.…”