@conference {324, title = {Improving Multi-label Classifiers via Label Reduction with Association Rules}, booktitle = {7th International Conference on Hybrid Artificial Intelligent Systems (HAIS 2012)}, year = {2012}, note = {TIN2008-06681-C06-02,TIC-3928}, month = {9}, pages = {188{\textendash}199}, address = {Salamanca (Spain)}, abstract = {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.}, isbn = {978-3-642-28930-9}, doi = {10.1007/978-3-642-28931-6_18}, author = {Francisco Charte and A.J. Rivera-Rivas and M. J. del Jesus and F. Herrera} }