Publications (4)0 Total impact
Conference Proceeding: A TDIDT technique for multi-label classification[show abstract] [hide abstract]
ABSTRACT: There are numerous problems of increasing significance where a pattern can have several classes simultaneously associated. This kind of problems, usually called multi-label problems, should be tackled with specific techniques in order to generate models more accurate than those obtained with classical classification algorithms. This work presents the adaptation of the J48 algorithm to multi-label classification. The developed algorithm allows the generation of interpretable models and has been tested over several datasets and experiments show that it has a performance which is similar to other multi-label tree-based approaches being specially suitable to be used as base-classifier in an ensemble.Intelligent Systems Design and Applications (ISDA), 2010 10th International Conference on; 01/2011
- Multiple-Valued Logic and Soft Computing. 01/2011; 17:183-206.
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ABSTRACT: The present work expounds a preliminary work of a genetic programming algorithm to deal with multi-label classification problems. The algorithm uses Gene Expression Programming and codifies a classification rule into each individual. A niching technique assures diversity in the population. The final classifier is made up by a set of rules for each label that determines if a pattern belongs or not to the label. The proposal have been tested over several domains and compared with other multi-label algorithms and the results shows that it is specially suitable to handle with nominal data sets.Hybrid Artificial Intelligence Systems, 5th International Conference, HAIS 2010, San Sebastián, Spain, June 23-25, 2010. Proceedings, Part II; 01/2010
Conference Proceeding: A Niching Algorithm to Learn Discriminant Functions with Multi-Label Patterns.Intelligent Data Engineering and Automated Learning - IDEAL 2009, 10th International Conference, Burgos, Spain, September 23-26, 2009. Proceedings; 01/2009