Sequential automatic search of subsets of classifiers in multi class classification

Multiclass Learning (ML) requires a classifier to discriminate instances (objects) among several classes of an outcome (response) variable. Most of the proposed methods for ML do not consider that analyzing complex datasets requires the results to be easily interpretable. We refer to the Sequential Automatic Search of Subset of Classifiers (SASSC) algorithm as an approach able to find the right compromise between knowledge extraction and good prediction. SASSC is an iterative algorithm that works by building a taxonomy of classes in an ascendant manner: this is done by the solution of a multiclass problem obtained by decomposing it into several r-nary problems (r >> 2) in an agglomerative way. We consider the use of different classification methods as base classifiers and evaluate the performance of SASSC with respect to alternative classes aggregation criteria which allow us to compare either the predictive performance or the interpretation issues related to the use of each set of classifiers.