Optimization of the ECOC matrix

Abstract

Error Correcting Output Coding (ECOC) is a classifier combination technique for multiclass classification problems. In this approach, several base classifiers are trained to learn different dichotomies of the classes, specified by the columns of a code matrix. These classifiers' output for an unknown pattern is compared to the codeword of each class which is the desired output of the dichotomizers, in an error correcting fashion. While ECOC is one of the best solutions to multiclass problems, the solution is suboptimal due to the fact that the code matrix and the dichotomizers are set or learned independently. In this paper, we show an iterative update algorithm for the code matrix that is designed to reduce this decoupling. It consists of updates to the initial code matrix so as to reduce the discrepancy between the code matrix and the output of the trained dichotomizers. We show that the proposed algorithm improves over the basic ECOC approach, for some well-known data sets.