Improving Multi-label Classifiers via Label Reduction with Association Rules

Multi-label classification is a generalization of well known problems, such as binary or multi-class classification, in a way that each processed instance is associated not with a class (label) but with a subset of these. In recent years different techniques have appeared which, through the transformation of the data or the adaptation of classic algorithms, aim to provide a solution to this relatively recent type of classification problem. This paper presents a new transformation technique for multi-label classification based on the use of association rules aimed at the reduction of the label space to deal with this problem.