Abstract
In this work, we address the task of multi-label classification (MLC). There are two main groups of methods addressing the task of MLC: problem transformation and algorithm adaptation. Methods from the former group transform the dataset to simpler local problems and then use off-the-shelf methods to solve them. Methods from the latter group change and adapt existing methods to directly address this task and provide a global solution. There is no consensus on when to apply a given method (local or global) to a given dataset. In this work, we design a method that builds on the strengths of both groups of methods. We propose an ensemble method that constructs global predictive models on randomly selected subsets of labels. More specifically, we extend the random forests of predictive clustering trees (PCTs) to consider random output subspaces. We evaluate the proposed ensemble extension on 13 benchmark datasets. The results give parameter recommendations for the proposed method and show that the method yields models with competitive performance as compared to three competing methods.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: Bagging, boosting, and variants. Mach. Learn. 36(1), 105–139 (1999)
Demšar, J.: Statistical comparisons of classifiers over multiple data sets. J. Mach. Learn. Res. 7, 1–30 (2006)
Joly, A., Geurts, P., Wehenkel, L.: Random forests with random projections of the output space for high dimensional multi-label classification. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS, vol. 8724, pp. 607–622. Springer, Heidelberg (2014). doi:10.1007/978-3-662-44848-9_39
Kocev, D., Vens, C., Struyf, J., Džeroski, S.: Tree ensembles for predicting structured outputs. Pattern Recogn. 46(3), 817–833 (2013)
Madjarov, G., Gjorgjevikj, D., Dimitrovski, I., Džeroski, S.: The use of data-derived label hierarchies in multi-label classification. J. Intel. Inf. Syst. 47(1), 57–90 (2016)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Džeroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012)
Szymański, P., Kajdanowicz, T., Kersting, K.: How is a data-driven approach better than random choice in label space division for multi-label classification? Entropy 18(8), 282 (2016)
Tsoumakas, G., Vlahavas, I.: Random k-labelsets: an ensemble method for multilabel classification. In: Kok, J.N., Koronacki, J., Mantaras, R.L., Matwin, S., Mladenič, D., Skowron, A. (eds.) ECML 2007. LNCS, vol. 4701, pp. 406–417. Springer, Heidelberg (2007). doi:10.1007/978-3-540-74958-5_38
Acknowledgements
We acknowledge the financial support of the Slovenian Research Agency via the grants P2-0103,L2-7509, and a young researcher grant to MB, as well as the European Commission, through grants ICT-2013-612944 MAESTRA and ICT-2013-604102 HBP SGA1.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2017 Springer International Publishing AG
About this paper
Cite this paper
Breskvar, M., Kocev, D., Džeroski, S. (2017). Multi-label Classification Using Random Label Subset Selections. In: Yamamoto, A., Kida, T., Uno, T., Kuboyama, T. (eds) Discovery Science. DS 2017. Lecture Notes in Computer Science(), vol 10558. Springer, Cham. https://doi.org/10.1007/978-3-319-67786-6_8
Download citation
DOI: https://doi.org/10.1007/978-3-319-67786-6_8
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-67785-9
Online ISBN: 978-3-319-67786-6
eBook Packages: Computer ScienceComputer Science (R0)