Abstract
Multi-label classification task has many applications in Text Categorization, Multimedia, Biology, Chemical data analysis and Social Network Mining, among others. Different approaches have been developed: Binary Relevance (BR), Label Power Set (LPS), Random k label sets (RAkEL), some of them consider the interaction between labels in a chain (Chain Classifier) and other alternatives around this method are derived, for instance, Probabilistic Chain Classifier, Monte Carlo Chain Classifier and Bayesian Chain Classifier (BCC). All previous approaches have in common and focus on is in considering different orders or combinations of the way the labels have to be predicted. Given that feature selection has proved to be important in classification tasks, reducing the dimensionality of the problem and even improving classification model’s accuracy. In this work a feature selection technique is tested in BCC algorithm with two searching methods, one using Best First (BF-FS-BCC) and another with GreedyStepwise (GS-FS-BCC), these methods are compared, the winner is also compared with BCC, both tests are compared through Wilcoxon Signed Rank test, in addition it is compared with others Chain Classifier and finally it is compared with others approaches (BR, RAkEL, LPS).
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Notes
- 1.
The implementation code is available in: https://github.com/R-Benitez-J/FS-BCC.
References
Dembczynski, K., Cheng, W., Hüllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains. In: ICML, vol. 10, pp. 279–286 (2010)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7(Jan), 1–30 (2006)
Hall, M.A.: Correlation-based feature selection for machine learning (1999)
Lastra, G., Luaces, O., Quevedo, J.R., Bahamonde, A.: Graphical feature selection for multilabel classification tasks. In: Gama, J., Bradley, E., Hollmén, J. (eds.) IDA 2011. LNCS, vol. 7014, pp. 246–257. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-24800-9_24
Read, J., Martino, L., Luengo, D.: Efficient Monte Carlo optimization for multi-label classifier chains. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 3457–3461. IEEE (2013)
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. Mach. Learn. 85(3), 333 (2011)
Read, J., Reutemann, P., Pfahringer, B., Holmes, G.: MEKA: a multi-label/multi-target extension to WEKA. J. Mach. Learn. Res. 17(21), 1–5 (2016). http://jmlr.org/papers/v17/12-164.html
Spolaôr, N., Cherman, E.A., Monard, M.C., Lee, H.D.: Filter approach feature selection methods to support multi-label learning based on relieff and information gain. In: Barros, L.N., Finger, M., Pozo, A.T., Gimenénez-Lugo, G.A., Castilho, M. (eds.) SBIA 2012. LNCS (LNAI), pp. 72–81. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-34459-6_8
SpolaôR, N., Cherman, E.A., Monard, M.C., Lee, H.D.: A comparison of multi-label feature selection methods using the problem transformation approach. Electron. Notes Theor. Comput. Sci. 292, 135–151 (2013)
Sucar, L.E., Bielza, C., Morales, E.F., Hernandez-Leal, P., Zaragoza, J.H., Larrañaga, P.: Multi-label classification with Bayesian network-based chain classifiers. Pattern Recogn. Lett. 41, 14–22 (2014)
Tsoumakas, G., Katakis, I., Vlahavas, I.: Random k-labelsets for multilabel classification. IEEE Trans. Knowl. Data Eng. 23(7), 1079–1089 (2011)
Tsoumakas, G., Katakis, I., et al.: Multi-label classification: an overview. Int. J. Data Warehous. Min. (IJDWM) 3(3), 1–13 (2007)
Witten, I.H., Frank, E., Hall, M.A., Pal, C.J.: Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, Burlington (2016)
Xu, H., Xu, L.: Multi-label feature selection algorithm based on label pairwise ranking comparison transformation. In: 2017 International Joint Conference on Neural Networks (IJCNN), pp. 1210–1217. IEEE (2017)
Zaragoza, J.H., Sucar, L.E., Morales, E.F., Bielza, C., Larranaga, P.: Bayesian chain classifiers for multidimensional classification. IJCAI, vol. 11, pp. 2192–2197 (2011)
Zhang, M.L., Peña, J.M., Robles, V.: Feature selection for multi-label naive Bayes classification. Inf. Sci. 179(19), 3218–3229 (2009)
Zhang, M.L., Zhou, Z.H.: Ml-KNN: a lazy learning approach to multi-label learning. Pattern Recogn. 40(7), 2038–2048 (2007)
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Jiménez, R.B., Morales, E.F., Escalante, H.J. (2018). Bayesian Chain Classifier with Feature Selection for Multi-label Classification. In: Batyrshin, I., Martínez-Villaseñor, M., Ponce Espinosa, H. (eds) Advances in Soft Computing. MICAI 2018. Lecture Notes in Computer Science(), vol 11288. Springer, Cham. https://doi.org/10.1007/978-3-030-04491-6_18
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