Abstract
In multi-label learning, each instance in the training set is associated with a set of labels, and the task is to output a label set for each unseen instance. This paper describes a new method for multi-label classification based on the Dempster-Shafer theory of belief functions to classify an unseen instance on the basis of its k nearest neighbors. The proposed method generalizes an existing single-label evidence-theoretic learning method to the multi-label case. In multi-label case, the frame of discernment is not the set of all possible classes, but it is the powerset of this set. That requires an extension of evidence theory to manipulate multi-labelled data. Using evidence theory makes us able to handle ambiguity and imperfect knowledge regarding the label sets of training patterns. Experiments on benchmark datasets show the efficiency of the proposed approach as compared to other existing methods.
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References
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recognition 37(9), 1757–1771 (2004)
Crammer, K., Singer, Y.: A Family of Additive Online Algorithms for Category Ranking. In: Proc. of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 151–158 (2002)
Denœux, T.: A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. on Systems, Man and Cybernetics 25(5), 804–813 (1995)
Denœux, T., Smets, P.: Classification using Belief Functions: the Relationship between the Case-based and Model-based Approaches. IEEE Trans. on Systems, Man and Cybernetics B 36(6), 1395–1406 (2006)
Denœux, T., Zouhal, L.M.: Handling possibilistic labels in pattern classification using evidential reasoning. Fuzzy Sets and Systems 122(3), 47–92 (2001)
Elisseeff, A., Weston, J.: Kernel methods for multi-labelled classification and categorical regression problems. Advances in Neural Information Processing Systems 14, 681–687 (2002)
Konstantinos, T., Tsoumakas, G., Kalliris, G., Vlahavas, I.: Multi-label classification of music into emotions. In: Proc. of the 9th International Conference on Music Information Retrieval (ISMIR 2008), Philadelphia, PA, USA (2008)
McCallum, A.: Multi-Label Text Classification with a Mixture Model Trained by EM. In: Working Notes of the AAAI 1999 Workshop on Text Learning (1999)
Schapire, R.E., Singer, Y.: BoosTexter: A Boosting-based System for Text Categorization. Machine Learning 39(2-3), 135–168 (2000)
Shafer, G.: A mathematical theory of evidence. Princeton University Press, Princeton (1976)
Smets, P.: The combination of evidence in the Transferable Belief Model. IEEE Trans. on Pattern Analysis and Machine Intelligence 12(5), 447–458 (1990)
Tsoumakas, G., Katakis, I.: Multi-Label Classification: An Overview. International Journal of Data Warehousing and Mining 3(3), 1–13 (2007)
Yang, Y.: An evaluation of statistical approaches to text categorization. Journal of Information Retrieval 1, 78–88 (1999)
Younes, Z., Abdallah, F., Denœux, T.: Multi-label classification algorithm derived from k-nearest neighbor rule with label dependencies. In: Proc. of the 16th European Signal Processing Conference, Lausanne, Switzerland, August 25–29 (2008)
Zhang, M.-L., Zhou, Z.-H.: ML-KNN:A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–3048 (2007)
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© 2009 Springer-Verlag Berlin Heidelberg
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Younes, Z., Abdallah, F., Denœux, T. (2009). An Evidence-Theoretic k-Nearest Neighbor Rule for Multi-label Classification. In: Godo, L., Pugliese, A. (eds) Scalable Uncertainty Management. SUM 2009. Lecture Notes in Computer Science(), vol 5785. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04388-8_23
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DOI: https://doi.org/10.1007/978-3-642-04388-8_23
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