Softmax regression for ECOC reconstruction

Classification by binary decomposition is a well-known method to solve multiclass classification tasks since a large number of algorithms were designed for binary classification. Once the polychotomy has been decomposed into several dichotomies, the decisions of binary learners on a test sample are aggregated by a reconstruction rule to set the final multiclass label. In this context, this paper presents a reconstruction rule based on softmax regression which considers the reconstruction task as a new classification problem. To this aim, as second-order features we use both the crisp labels and the reliabilities of binary decisions. Six heterogeneous datasets and three different classification architectures have been used to test our method, whose performance favorably compare with those provided by other three reconstruction rules both in terms of global accuracy and geometric mean of accuracies.