Solving biomedical classification tasks by softmax reconstruction in ECOC framework

Several medical and biological applications face with multiclass recognition problems. Such polychotomies can be addressed by decomposition techniques, which reduce the polychotomy into a series of dichotomies and then provide the final multiclass label using a reconstruction rule. Within this framework, we present a reconstruction rule based on softmax regression, where the features of the new classification task are the crisp labels and the reliabilities of dichotomizers´ classifications. The approach has been tested on six medical and biological datasets, decomposing the polychotomies via the Error-Correcting Output Code. Its performances favorably compare with those provided by other two well-known reconstruction rules both in terms of global accuracy and accuracy per class.