Non-competence reliability in multi-classification based on error-correcting output codes

Error-correcting output code (ECOC) has been an established technique for multi-classification due to its simpleness and efficiency. However, the non-competent classifiers emerge when they classify an instance whose real class does not belong to one of the subclass sets which are used to learn the classifier. In this regard, in order to analyse the non-competence problem in the ECOC decomposing framework, a new weighted decoding strategy based on classifiers' competence ability is presented as the solution, which can strengthen the influence of competent classifiers and reduce that of non-competent ones on classification performance through learning weight coefficient of base classifiers. Meanwhile, the support vector data description is applied to compute the distance of instances to each class. The statistic simulations based on UCI datasets corroborate the proposed method. © 2017, Editorial Office of Systems Engineering and Electronics. All right reserved.