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.
Keywords
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Both have be run with -d 0.1, leaving all the other parameters set to default value.
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RAkEL, resp. CC, has been executed with Support Vector Machines with polynomial kernel, resp. with C4.5 decision trees, as base classifier.
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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].
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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
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