Abstract
Multi-label classification (MLC) is a challenging task in machine learning consisting in the prediction of multiple labels associated with a single instance. Promising approaches for MLC are those able to capture label dependencies by learning a single probabilistic model—differently from other competitive approaches requiring to learn many models. The model is then exploited to compute the most probable label configuration given the observed attributes. Cutset Networks (CNets) are density estimators leveraging context-specific independencies providing exact inference in polynomial time. The recently introduced Extremely Randomized CNets (XCNets) reduce the structure learning complexity making able to learn ensembles of XCNets outperforming state-of-the-art density estimators. In this paper we employ XCNets for MLC by exploiting efficient Most Probable Explanations (MPE). An experimental evaluation on real-world datasets shows how the proposed approach is competitive w.r.t. other sophisticated methods for MLC.
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
Notes
- 1.
- 2.
- 3.
- 4.
- 5.
Both have be run with -d 0.1, leaving all the other parameters set to default value.
- 6.
RAkEL, resp. CC, has been executed with Support Vector Machines with polynomial kernel, resp. with C4.5 decision trees, as base classifier.
- 7.
We executed the code avalible at https://github.com/giulianavll/MLC-SPN to reproduce the results reported in this paper. The algorithm used for learning the structure of SPNs corresponds to that reported in [26].
References
Antonucci, A., Corani, G., Mauá, D.D., Gabaglio, S.: An ensemble of Bayesian networks for multilabel classification. In: IJCAI, pp. 1220–1225 (2013)
Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning multi-label scene classification. Pattern Recogn. 37(9), 1757–1771 (2004)
Cano, A., Luna, J.M., Gibaja, E.L., Ventura, S.: LAIM discretization for multi-label data. Inf. Sci. 330, 370–384 (2016)
Chow, C., Liu, C.: Approximating discrete probability distributions with dependence trees. IEEE Trans. Inf. Theory 14(3), 462–467 (1968)
Conaty, D., de Campos, C.P., Mauá, D.D.: Approximation complexity of maximum a posteriori inference in sum-product networks. In: UAI (2017)
Corani, G., Antonucci, A., Mauá, D.D., Gabaglio, S.: Trading off speed and accuracy in multilabel classification. In: van der Gaag, L.C., Feelders, A.J. (eds.) PGM 2014. LNCS (LNAI), vol. 8754, pp. 145–159. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-11433-0_10
Dembczyński, K., Cheng, W., Hüllermeier, E.: Bayes optimal multilabel classification via probabilistic classifier chains. In: ICML, pp. 279–286 (2010)
Dembczyński, K., Waegeman, W., Cheng, W., Hüllermeier, E.: On label dependence and loss minimization in multi-label classification. Mach. Learn. 88(1), 5–45 (2012)
Di Mauro, N., Vergari, A., Basile, T.M.A.: Learning Bayesian random cutset forests. In: Esposito, F., Pivert, O., Hacid, M.-S., Raś, Z.W., Ferilli, S. (eds.) ISMIS 2015. LNCS (LNAI), vol. 9384, pp. 122–132. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-25252-0_13
Di Mauro, N., Vergari, A., Basile, T.M.A., Esposito, F.: Fast and accurate density estimation with extremely randomized cutset networks. In: Ceci, M., Hollmén, J., Todorovski, L., Vens, C., Džeroski, S. (eds.) ECML PKDD 2017. LNCS (LNAI), vol. 10534, pp. 203–219. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71249-9_13
Di Mauro, N., Vergari, A., Esposito, F.: Learning accurate cutset networks by exploiting decomposability. In: Gavanelli, M., Lamma, E., Riguzzi, F. (eds.) AI*IA 2015. LNCS (LNAI), vol. 9336, pp. 221–232. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24309-2_17
Di Mauro, N., Vergari, A., Esposito, F.: Multi-label classification with cutset networks. In: PGM, vol. 52, pp. 147–158 (2016)
Koller, D., Friedman, N.: Probabilistic Graphical Models: Principles and Techniques. MIT Press, Cambridge (2009)
Llerena, J.V., Mauá, D.D.: On using sum-product networks for multi-label classification. In: BRACIS, pp. 25–30 (2017)
Lowd, D., Rooshenas, A.: The libra toolkit for probabilistic models. JMLR 16, 2459–2463 (2015)
Madjarov, G., Kocev, D., Gjorgjevikj, D., Deroski, S.: An extensive experimental comparison of methods for multi-label learning. Pattern Recogn. 45(9), 3084–3104 (2012)
Meila, M., Jordan, M.I.: Learning with mixtures of trees. JMLR 1, 1–48 (2000)
Peharz, R., Gens, R., Pernkopf, F., Domingos, P.: On the latent variable interpretation in sum-product networks. IEEE Trans. Pattern Anal. Mach. Intell. 39(10), 2030–2044 (2017)
Poon, H., Domingos, P.: Sum-product network: a new deep architecture. In: NIPS 2010 Workshop on Deep Learning and Unsupervised Feature Learning (2011)
Rahman, T., Gogate, V.: Learning ensembles of cutset networks. In: AAAI (2016)
Rahman, T., Kothalkar, P., Gogate, V.: Cutset networks: a simple, tractable, and scalable approach for improving the accuracy of Chow-Liu trees. In: Calders, T., Esposito, F., Hüllermeier, E., Meo, R. (eds.) ECML PKDD 2014. LNCS (LNAI), vol. 8725, pp. 630–645. Springer, Heidelberg (2014). https://doi.org/10.1007/978-3-662-44851-9_40
Read, J., Pfahringer, B., Holmes, G., Frank, E.: Classifier chains for multi-label classification. In: Buntine, W., Grobelnik, M., Mladenić, D., Shawe-Taylor, J. (eds.) ECML PKDD 2009. LNCS (LNAI), vol. 5782, pp. 254–269. Springer, Heidelberg (2009). https://doi.org/10.1007/978-3-642-04174-7_17
Roth, D.: On the hardness of approximate reasoning. Artif. Intell. 82(1–2), 273–302 (1996)
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., Vlahavas, I.: Mining multi-label data. In: Maimon, O., Rokach, L. (eds.) Data Mining and Knowledge Discovery Handbook, 2nd edn, pp. 667–685. Springer, Boston (2009). https://doi.org/10.1007/978-0-387-09823-4_34
Vergari, A., Di Mauro, N., Esposito, F.: Simplifying, regularizing and strengthening sum-product network structure learning. In: Appice, A., Rodrigues, P.P., Santos Costa, V., Gama, J., Jorge, A., Soares, C. (eds.) ECML PKDD 2015. LNCS (LNAI), vol. 9285, pp. 343–358. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-23525-7_21
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Basile, T.M.A., Di Mauro, N., Esposito, F. (2018). Extremely Randomized CNets for Multi-label Classification. In: Ghidini, C., Magnini, B., Passerini, A., Traverso, P. (eds) AI*IA 2018 – Advances in Artificial Intelligence. AI*IA 2018. Lecture Notes in Computer Science(), vol 11298. Springer, Cham. https://doi.org/10.1007/978-3-030-03840-3_25
Download citation
DOI: https://doi.org/10.1007/978-3-030-03840-3_25
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-03839-7
Online ISBN: 978-3-030-03840-3
eBook Packages: Computer ScienceComputer Science (R0)