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Progress in Artificial Intelligence

Erschienen in:

01.12.2012 | Regular Paper

Binary relevance efficacy for multilabel classification

verfasst von: Oscar Luaces, Jorge Díez, José Barranquero, Juan José del Coz, Antonio Bahamonde

Erschienen in: Progress in Artificial Intelligence | Ausgabe 4/2012

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Abstract

The goal of multilabel (ML) classification is to induce models able to tag objects with the labels that better describe them. The main baseline for ML classification is binary relevance (BR), which is commonly criticized in the literature because of its label independence assumption. Despite this fact, this paper discusses some interesting properties of BR, mainly that it produces optimal models for several ML loss functions. Additionally, we present an analytical study of ML benchmarks datasets and point out some shortcomings. As a result, this paper proposes the use of synthetic datasets to better analyze the behavior of ML methods in domains with different characteristics. To support this claim, we perform some experiments using synthetic data proving the competitive performance of BR with respect to a more complex method in difficult problems with many labels, a conclusion which was not stated by previous studies.

Vorheriger Artikel “I can tell you what it’s not”: active learning from counterexamples

Nächster Artikel Information Theoretic Learning and local modeling for binary and multiclass classification

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It is available online at http://www.aic.uniovi.es/ml_generator.

Website: http://www.aic.uniovi.es/ml_generator.

Bielza, C., Li, G., Larrañaga, P.: Multi-dimensional classification with bayesian networks. Int. J. Approx. Reason. 52(6), 705–727 (2011)MATHCrossRef

Chang, C.-C., Lin, C.-J.: LIBSVM: a library for support vector machines. ACM Trans. Intelli. Syst. Technol. 2, 27:1–27:27. Available at http://www.csie.ntu.edu.tw/~cjlin/libsvm/ (2011)

Cheng, W., Hüllermeier, E.: Combining instance-based learning and logistic regression for multilabel classification. Mach. Learn. 76(2), 211–225 (2009)CrossRef

Dembczyński, K., Cheng, W., Hüllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains. In: Proceedings of the 27th International Conference on Machine Learning (ICML) pp. 279–286 (2010)

Dembczyński, K., Waegeman, W., Cheng, W., Hüllermeier, E.: An exact algorithm for f-measure maximization. In: Proceedings of the Neural Information Processing Systems (NIPS), pp. 1404–1412 (2011)

Elisseeff, A., Weston, J.: A kernel method for multi-labelled classification. In: Advances in Neural Information Processing Systems 14, MIT Press, Cambridge, pp. 681–687 (2001)

Godbole, S., Sarawagi, S.: Discriminative methods for multi-labeled classification. In: Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science), vol. 3056, pp. 22–30 (2004)

Joachims, T.: A support vector method for multivariate performance measures. In: Proceedings of the ICML ’05, pp. 377–384 (2005)

Lastra, G., Luaces, O., Quevedo, J., Bahamonde, A.: Graphical feature selection for multilabel classification tasks. In: Gama, J., Bradley, E., Hollmén, J. (eds.) Proceedings of Advances in Intelligent Data Analysis X (IDA 2011). Springer, Lecture Notes in Computer Science, vol. 7014, 246–257 (2011)

10.

Madjarov, G., Kocev, D., Gjorgjevikj, D., Deroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recognit. 45(9), 3084–3104 (2012)CrossRef

11.

Montañés, E., Quevedo, J., del Coz, J.: Aggregating independent and dependent models to learn multi-label classifiers. In: Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases (ECLM-PKDD) pp. 484–500 (2011)

12.

Petterson, J., Caetano, T.: Reverse multi-label learning. Adv. Neural Inform. Process. Syst. 23, 1912–1920 (2010)

13.

Quevedo, J.R., Luaces, O., Bahamonde, A.: Multilabel classifiers with a probabilistic thresholding strategy. Pattern Recognit. 45(2), 876–883 (2012)MATH

14.

Read, J., Pfahringer, B., Holmes, G.: Generating synthetic multi-label data streams. In: ECML/PKKD 2009 Workshop on Learning from Multi-label Data (MLD’09), pp. 69–84 (2009a)

15.

Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Proceedings of European Conference on Machine Learning and Knowledge Discovery in Databases (ECML-PKDD), pp. 254–269 (2009b)

16.

Read, J., Bifet, A., Holmes, G., Pfahringer, B.: Streaming multi-label classification. JMLR Workshop and Conference Proceedings (Second Workshop on Applications of Pattern Analysis), vol. 17, pp. 19–25 (2011a)

17.

Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333–359 (2011b)CrossRef

18.

Schapire, R., Singer, Y.: Boostexter: a boosting-based system for text categorization. Mach. Learn. 39(2), 135–168 (2000)MATHCrossRef

19.

Tsoumakas, G., Katakis, I.: Multi label classification: an overview. Int. J. Data Warehousing Min. 3(3), 1–13 (2007)CrossRef

20.

Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multilabel data. Data Mining and Knowledge Discovery Handbook pp. 667–685 (2010a)

21.

Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multi-label classification. IEEE Trans. Knowl. Discov. Data Eng. 23(7), 1079–1089 (2010b)CrossRef

22.

Zaragoza, J., Sucar, L., Bielza, C., Larrañaga, P.: Bayesian chain classifiers for multidimensional classification. In: Twenty-Second International Joint Conference on Artificial Intelligence (IJCAI), pp. 2192–2197 (2011)

23.

Zhang, M.L., Zhou, Z.: ML-KNN: a lazy learning approach to multi-label learning. Pattern Recognit. 40(7), 2038–2048 (2007)MATHCrossRef

Titel: Binary relevance efficacy for multilabel classification
verfasst von: Oscar Luaces
Jorge Díez
José Barranquero
Juan José del Coz
Antonio Bahamonde
Publikationsdatum: 01.12.2012
Verlag: Springer-Verlag
Erschienen in: Progress in Artificial Intelligence / Ausgabe 4/2012
Print ISSN: 2192-6352
Elektronische ISSN: 2192-6360
DOI: https://doi.org/10.1007/s13748-012-0030-x

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