Abstract
Learning from crowds is a recently fashioned supervised classification framework where the true/real labels of the training instances are not available. However, each instance is provided with a set of noisy class labels, each indicating the class-membership of the instance according to the subjective opinion of an annotator. The additional challenges involved in the extension of this framework to the multi-label domain are explored in this paper. A solution to this problem combining a Structural EM strategy and the multi-dimensional Bayesian network models as classifiers is presented.
Using real multi-label datasets adapted to the crowd framework, the designed experiments try to shed some lights on the limits of learning to classify from the multiple and imprecise information of supervision.
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Bielza, C., Li, G., Larrañaga, P.: Multi-dimensional classification with Bayesian networks. International Journal of Approximate Reasoning 52(6), 705–727 (2011)
Brodley, C.E., Friedl, M.A.: Identifying mislabeled training data. Journal of Artificial Intelligence Research 11, 131–167 (1999)
Cour, T., Sapp, B., Taskar, B.: Learning from partial labels. Journal of Machine Learning Research 12, 1501–1536 (2011)
Dempster, A.P., Laird, N.M., Rubin, D.B.: Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society. Series B (Methodological) 39(1), 1–38 (1977)
Friedman, N.: Learning belief networks in the presence of missing values and hidden variables. In: Proceedings of the 14th ICML, pp. 125–133 (1997)
López-Cruz, P.L., Larrañaga, P., DeFelipe, J., Bielza, C.: Bayesian network modeling of the consensus between experts: An application to neuron classification. International Journal of Approximate Reasoning (in press, 2013)
McLachlan, G.J., Krishnan, T.: The EM Algorithm and Extensions (Wiley Series in Probability and Statistics). Wiley Interscience (1997)
Nguyen, Q., Valizadegan, H., Hauskrecht, M.: Learning classification with auxiliary probabilistic information. In: Proceedings of the 11th IEEE International Conference on Data Mining (ICDM 2011), pp. 477–486 (2011)
Raykar, V.C., Yu, S., Zhao, L.H., Valadez, G.H., Florin, C., Bogoni, L., Moy, L.: Learning from crowds. Journal of Machine Learning Research 11, 1297–1322 (2010)
Rodríguez, J.D., Martínez, A.P., Arteta, D., Tejedor, D., Lozano, J.A.: Using multidimensional bayesian network classifiers to assist the treatment of multiple sclerosis. IEEE Transactions on Systems, Man, and Cybernetics 42(6), 1705–1715 (2012)
Sellamanickam, S., Tiwari, C., Selvaraj, S.K.: Regularized structured output learning with partial labels. In: Proceedings of the 12th SDM, pp. 1059–1070 (2012)
Sheng, V.S., Provost, F.J., Ipeirotis, P.G.: Get another label? improving data quality and data mining using multiple, noisy labelers. In: Proceedings of the International Conference on Knowledge Discovery and Data Mining, pp. 614–622 (2008)
Smyth, P., Fayyad, U., Burl, M., Perona, P., Baldi, P.: Inferring ground truth from subjective labelling of venus images. In: Proceedings of the Advances in Neural Information Processing Systems (NIPS), pp. 1085–1092 (1994)
Snow, R., O’Connor, B., Jurafsky, D., Ng, A.Y.: Cheap and fast - but is it good? evaluating non-expert annotations for natural language tasks. In: Proceedings of the Conference on Empirical Methods in NLP, pp. 254–263 (2008)
Sun, Y.Y., Zhang, Y., Zhou, Z.H.: Multi-label learning with weak label. In: Proceedings of the 24th AAAI Conference on Artificial Intelligence, AAAI 2010 (2010)
Younes, Z., abdallah, F., Denœux, T.: Evidential multi-label classification approach to learning from data with imprecise labels. In: Hüllermeier, E., Kruse, R., Hoffmann, F. (eds.) IPMU 2010. LNCS, vol. 6178, pp. 119–128. Springer, Heidelberg (2010)
Zhang, M.L., Zhou, Z.H.: A review on multi-label learning algorithms. IEEE Transactions on Knowledge and Data Engineering ( in press, 2013)
Zhu, X., Wu, X., Chen, Q.: Eliminating class noise in large datasets. In: Proceedings of the 20th ICML, pp. 920–927 (2003)
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Hernández-González, J., Inza, I., Lozano, J.A. (2013). Learning from Crowds in Multi-dimensional Classification Domains. In: Bielza, C., et al. Advances in Artificial Intelligence. CAEPIA 2013. Lecture Notes in Computer Science(), vol 8109. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-40643-0_36
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DOI: https://doi.org/10.1007/978-3-642-40643-0_36
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