Effectiveness of error correcting output codes in multiclass learning problems

In the framework of decomposition methods for multiclass classification problems, error correcting output codes (ECOC) can be fruitfully used as codewords for coding classes in order to enhance the generalization capability of learning machines. The effectiveness of error correcting output codes depends mainly on the independence of code-word bits and on the accuracy by which each dichotomy is learned. Separated and non-linear dichotomizers can improve the independence among computed codeword bits, thus fully exploiting the error recovering capabilities of ECOC. In the experimentation presented in this paper we compare ECOC decomposition methods implemented through monolithic multi-layer perceptrons and sets of linear and non-linear independent dichotomizers. The most effectiveness of ECOC decomposition scheme is obtained by Parallel Non-linear Dichotomizers (PND), a learning machine based on decomposition of polychotomies into dichotomics, using non linear independent dichotomizers.