Abstract
Despite that the majority of machine learning approaches aim to solve binary classification problems, several real-world applications require specialized algorithms able to handle many different classes, as in the case of single-label multi-class and multi-label classification problems. The Label Ranking framework is a generalization of the above mentioned settings, which aims to map instances from the input space to a total order over the set of possible labels. However, generally these algorithms are more complex than binary ones, and their application on large-scale datasets could be untractable.
The main contribution of this work is the proposal of a novel general on-line preference-based label ranking framework. The proposed framework is able to solve binary, multi-class, multi-label and ranking problems. A comparison with other baselines has been performed, showing effectiveness and efficiency in a real-world large-scale multi-label task.
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References
Aiolli, F.: Large margin multiclass learning: models and algorithms. Ph.D. thesis, Department of Computer Science, University of Pisa (2004)
Aiolli, F., Sperduti, A.: Learning preferences for multiclass problems. In: Advances in Neural Information Processing Systems, pp. 17–24 (2005)
Allan, J.: Topic Detection and Tracking: Event-Based Information Organization, vol. 12. Springer, Heidelberg (2012)
Brinker, K., Fürnkranz, J., Hüllermeier, E.: A unified model for multilabel classification and ranking. In: Proceedings of the 2006 conference on ECAI 2006: 17th European Conference on Artificial Intelligence, 29 August - 1 September 2006, Riva del Garda, Italy, pp. 489–493. IOS Press (2006)
Chu, W., Ghahramani, Z.: Preference learning with gaussian processes. In: Proceedings of the 22nd International Conference On Machine learning, pp. 137–144. ACM (2005)
Dembczynski, K., Cheng, W., Hüllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains. In: ICML, vol. 10, pp. 279–286 (2010)
Freund, Y., Schapire, R.E.: Large margin classification using the perceptron algorithm. Mach. Learn. 37(3), 277–296 (1999)
Hsu, C.W., Lin, C.J.: A comparison of methods for multiclass support vector machines. IEEE Trans. Neural Netw. 13(2), 415–425 (2002)
Meier, U., Ciresan, D.C., Gambardella, L.M., Schmidhuber, J.: Better digit recognition with a committee of simple neural nets. In: 2011 International Conference on Document Analysis and Recognition (ICDAR), pp. 1250–1254. IEEE (2011)
Nentidis, A., Bougiatiotis, K., Krithara, A., Paliouras, G., Kakadiaris, I.: Results of the fifth edition of the BioASQ challenge. In: BioNLP 2017, pp. 48–57. Association for Computational Linguistics, Vancouver, August 2017
Ou, G., Murphey, Y.L.: Multi-class pattern classification using neural networks. Pattern Recognit. 40(1), 4–18 (2007)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)
Tsoumakas, G., Katakis, I.: Multi-label classification: an overview. Int. J. Data Warehous. Min. 3(3), 1–13 (2006)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS (LNAI), vol. 4701, pp. 406–417. Springer, Heidelberg (2007). https://doi.org/10.1007/978-3-540-74958-5_38
Vembu, S., Gärtner, T.: Label ranking algorithms: a survey. In: Fürnkranz, J., Hüllermeier, E. (eds.) Preference Learning, pp. 45–64. Springer, Heidelberg (2010). https://doi.org/10.1007/978-3-642-14125-6_3
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Trans. Knowl. Data Eng. 26(8), 1819–1837 (2014)
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Lauriola, I., Polato, M., Lavelli, A., Rinaldi, F., Aiolli, F. (2018). Learning Preferences for Large Scale Multi-label Problems. In: Kůrková, V., Manolopoulos, Y., Hammer, B., Iliadis, L., Maglogiannis, I. (eds) Artificial Neural Networks and Machine Learning – ICANN 2018. ICANN 2018. Lecture Notes in Computer Science(), vol 11139. Springer, Cham. https://doi.org/10.1007/978-3-030-01418-6_54
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