Read, J., Pfahringer, B. & Holmes, G. (2008). Multi-label classification using ensembles of pruned sets. In Proceedings of the 8th IEEE International Conference on Data Mining, 15-19 December, 2008 (pp. 995-1000). Washington, DC, USA: IEEE.
Permanent Research Commons link: http://hdl.handle.net/10289/8077
This paper presents a Pruned Sets method (PS) for multi-label classification. It is centred on the concept of treating sets of labels as single labels. This allows the classification process to inherently take into account correlations between labels. By pruning these sets, PS focuses only on the most important correlations, which reduces complexity and improves accuracy. By combining pruned sets in an ensemble scheme (EPS), new label sets can be formed to adapt to irregular or complex data. The results from experimental evaluation on a variety of multi-label datasets show that [E]PS can achieve better performance and train much faster than other multi-label methods.
©2008 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.