Abstract
All the techniques discussed in the previous chapters assumed that each example is labeled with one and only one class. In realistic applications, however, this is not always the case. Quite often, an example is known to belong to two or more classes at the same time, sometimes to many classes. For machine learning, this poses certain new problems. After a brief discussion of how to deal with this issue within the framework of classical paradigms, this chapter describes the currently most popular approach: binary relevance.
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Notes
- 1.
The reader will recall that the target values 0. 8 and 0. 2 are more appropriate for the backpropagation-of-error algorithm than 1 and 0. See Chap. 5.
- 2.
The reader has noticed that the issue is similar to the one we have encountered in the section dealing with classifier chains.
References
Boutell, M. R., Luo, J., Shen, X., & Brown, C. M. (2004). Learning multi-label scene classification. Pattern Recognition, 37, 1757–1771
Clare, A. & King, R. D. (2001). Knowledge discovery in multi-label phenotype data. In Proceedings of the 5th European conference on principles of data mining and knowledge discovery, PKDD’01, Freiburg, Germany (pp. 42–53)
Godbole, S. & Sarawagi, S. (2004). Discriminative methods for multi-label classification. In H. Dai, R. Srikant, & C. Zhang (Eds.), Lecture Notes in Artificial Intelligence, Berlin, Heidelberg: Springer (Vol. 3056, pp. 22–30)
Koller, D. & Sahami, M. (1997). Hierarchically classifying documents using very few words. In Proceedings of the 14th International conference on machine learning, ICML’07, San Francisco, USA (pp. 170–178)
McCallum, A. (1999). Multi-label text classification with a mixture model trained by EM. In Proceedings of the workshop on text learning (AAAI’99) (pp. 1–7).
Read, J., Pfahringer, B., Holmes, G., & Frank, E. (2011). Classifier chains for multi-label classification. Machine Learning, 85, 333–359.
Zhang, M.-L. & Zhou, Z.-H. (2007). ML-KNN: A lazy learing approach to multi-label learning. Pattern Recognition, 40, 2038–2048.
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Kubat, M. (2017). Induction in Multi-Label Domains. In: An Introduction to Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-319-63913-0_13
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DOI: https://doi.org/10.1007/978-3-319-63913-0_13
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