Abstract
Inducing a classification function from a set of examples in the form of labeled instances is a standard problem in supervised machine learning. In this paper, we are concerned with ambiguous label classification (ALC), an extension of this setting in which several candidate labels may be assigned to a single example. By extending three concrete classification methods to the ALC setting and evaluating their performance on benchmark data sets, we show that appropriately designed learning algorithms can successfully exploit the information contained in ambiguously labeled examples. Our results indicate that the fundamental idea of the extended methods, namely to disambiguate the label information by means of the inductive bias underlying (heuristic) machine learning methods, works well in practice.
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References
Bennet, K., Demiriz, A.: Semi-supervised support vector machines. In: Advances in Neural Information Processing, vol. 11, pp. 368–374. MIT Press, Cambridge (1999)
Cavarelli, J., et al.: The structure of Staphylococcus aureus epidermolytic toxin A, an atypic serine protease, at 1.7 A resolution. Structure 5(6), 813–824 (1997)
Clark, P., Boswell, R.: Rule induction with CN2: Some recent improvements. In: Proc. 5th Europ. Working Session of Learning, Porto, pp. 151–163 (1991)
Clark, P., Niblett, T.: The CN2 induction algorithm. Machine Learning 3, 261–283 (1989)
Cohen, W.W.: Fast effective rule induction. In: Proc. 12th ICML, Tahoe City, CA, pp. 115–123 (1995)
Dasarathy, B.V. (ed.): Nearest Neighbor (NN) Norms: NN Pattern Classification Techniques. Computer Society Press, Los Alamitos (1991)
Dietterich, T.G., Lathrop, R.H., Lozano-Perez, T.: Solving the multiple-instance problem with axis-parallel rectangles. Art. Intell. Journal 89 (1997)
Domingos, P.: The role of Occam’s razor in knowledge discovery. Data Mining and Knowledge Discovery 3, 409–425 (1999)
Fayyad, U., Irani, K.B.: Multi-interval discretization of continuos attributes as preprocessing for classification learning. In: Proc. IJCAI 1993, pp. 1022–1027 (1993)
Fürnkranz, J.: Separate-and-conquer rule learning. AI Review 13(1), 3–54 (1999)
Grandvalet, Y.: Logistic regression for partial labels. In: IPMU 2002, Annecy, France, pp. 1935–1941 (2002)
Hüllermeier, E., Beringer, J.: Learning decision rules from positive and negative preferences. In: IPMU 2004, Perugia, Italy (2004)
Jin, R., Ghahramani, Z.: Learning with multiple labels. In: NIPS 2002, Vancouver, Canada (2002)
McCallum, A.: Multi-label text classification with a mixture model trained by EM. In: AAAI 1999 Workshop on Text Learning (1999)
Quinlan, J.R.: C4.5: Programs for Machine Learning. Morgan Kaufmann, San Francisco (1993)
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Hüllermeier, E., Beringer, J. (2005). Learning from Ambiguously Labeled Examples. In: Famili, A.F., Kok, J.N., Peña, J.M., Siebes, A., Feelders, A. (eds) Advances in Intelligent Data Analysis VI. IDA 2005. Lecture Notes in Computer Science, vol 3646. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11552253_16
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DOI: https://doi.org/10.1007/11552253_16
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-28795-7
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