Coding design for error correcting output codes based on perceptron

Abstract

It is known that error-correcting output codes (ECOC) is a common way to model multiclass classification problems, in which the research of encoding based on data is attracting more and more attention. We propose a method for learning ECOC with the help of a single-layered perception neural network. To achieve this goal, the code elements of ECOC are mapped to the weights of network for the given decoding strategy, and an object function with the constrained weights is used as a cost function of network. After the training, we can obtain a coding matrix including lots of subgroups of class. Experimental results on artificial data and University of California Irvine with logistic linear classifier and support vector machine as the binary learner show that our scheme provides better performance of classification with shorter length of coding matrix than other state-of-the-art encoding strategies.