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.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Boutell, M.R., Shen, J., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)
Denœux, T., Masson, M.-H.: Evidential reasoning in large partially ordered sets. Application to multi-label classification, ensemble clustering and preference aggregation. Annals of Operations Research (2011) (accepted for publication), doi:10.1007/s10479-011-0887-2
Denoeux, T., Younes, Z., Abdallah, F.: Representing uncertainty on set-valued variables using belief functions. Artificial Intelligence 174, 479–499 (2010)
Ghamrawi, N., McCallum, A.: Collective multi-label classification. In: 14th ACM International Conference on Information and Knowledge Management (2005)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Proc. of the 20th European Conference on Machine Learning, ECML 2009 (2009)
Schapire, R., Singer, Y.: Boostexter: a boosting-based system for text categorization. Machine Learning 39, 135–168 (2000)
Trohidis, K., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multilabel classification of music into emotions. In: Proc. 9th International Conference on Music Information Retrieval (ISMIR 2008), pp. 325–330 (2008)
Tsoumakas, G., Katakis, I.: Multi-label classification: An overview. International Journal of Data Warehousing and Mining 3(3), 1–13 (2007)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: An ensemble method for multilabel classification. In: Proc. 18th European Conference on Machine Learning, September 17-21 (2007)
Younes, Z., Abdallah, F., Denoeux, T., Snoussi, H.: A dependent multilabel classification method derived from the k-nearest neighbor rule. EURASIP Journal on Advances in Signal Processing, Article ID 645964, 14 (2011), doi:10.1155/2011/645964
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
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
Download citation
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
eBook Packages: EngineeringEngineering (R0)