Abstract
Multi-label classification deals with problems in which each instance can be associated with a set of labels. An effective multi-label method, named RAkEL, randomly breaks the initial set of labels into smaller sets and trains a single-label classifier in each of this subset. To classify an unseen instance, the predictions of all classifiers are combined using a voting process. In this paper, we adapt the RAkEL approach under the belief function framework applied to set-valued variables. Using evidence theory makes us able to handle lack of information by associating a mass function to each classifier and combining them conjunctively. Experiments on real datasets demonstrate that our approach improves classification performances.
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© 2012 Springer-Verlag Berlin Heidelberg
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Kanj, S., Abdallah, F., Denœux, T. (2012). Evidential Multi-label Classification Using the Random k-Label Sets Approach. In: Denoeux, T., Masson, MH. (eds) Belief Functions: Theory and Applications. Advances in Intelligent and Soft Computing, vol 164. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29461-7_2
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DOI: https://doi.org/10.1007/978-3-642-29461-7_2
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-29460-0
Online ISBN: 978-3-642-29461-7
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