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Title: Random k-Labelsets: An Ensemble Method for Multilabel Classification
Author(s): G. Tsoumakas, I. Vlahavas.
Appeared in: Proceedings of the 18th European Conference on Machine Learning (ECML 2007), J.N. Kok, J. Koronacki, R.L. de Mantaras, S. Matwin, D. Mladenic, A. Skowron (Ed.), Springer Verlag, LNAI 4701, pp. 406-417, Warsaw, Poland, 2007.
Abstract: This paper proposes an ensemble method for multilabel classification. The RAndom k-labELsets (RAKEL) algorithm constructs each member of the ensemble by considering a small random subset of labels and learning a single-label classifier for the prediction of each element in the powerset of this subset. In this way, the proposed algorithm aims to take into account label correlations using single-label classifiers that are applied on subtasks with manageable number of labels and adequate number of examples per label. Experimental results on common multilabel domains involving protein, document and scene classification show that better performance can be achieved compared to popular multilabel classification approaches.
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