Combined methods in multi-label classification algorithms

A classification rule is performed to assign a class to new sample indi- viduals. Many times the number of classes is large, consequently the classification rule could have some problem in reaching a satisfying level of accuracy. We deal with an approach called Sequential Automatic Search of a Subset of Classifiers (SASSC), able to enhance the classification rule performance and the interpretability of its output. It consists in splitting a classification problem among C classes into K < C less complex two-classes sub-problems and evaluate its performance on two different datasets. The main contributions of SASSC concern the new criteria for the aggregation of classes and super-classes and the alternative criteria for the estimation of the response class for unseen (test-set) observation.