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Naive Bayes Classification for Subset Selection in a Multi-label Setting

Mossina, Luca and Rachelson, Emmanuel Naive Bayes Classification for Subset Selection in a Multi-label Setting. (2018) In: International Conference on Pattern Recognition and Artificial Intelligence - ICPRAI 2018, 14 May 2018 - 17 May 2018 (Montréal, Canada).

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This article introduces a novel probabilistic formulation of multi-label classification based on the Bayes theorem. Under the naive hypothesis of conditional independence of features given the labels, a pseudo-bayesian inference approach is adopted, known as Naive Bayes. The prediction consists of two steps: the estimation of the size of the target label set and the selection of the elements of this set. This approach is implemented in the \nbx algorithm, an extension of naive Bayes into the multi-label domain. Its properties are discussed and evaluated on real-world data.

Item Type:Conference or Workshop Item (Paper)
Audience (conference):International conference proceedings
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Institution:Université de Toulouse > Institut Supérieur de l'Aéronautique et de l'Espace - ISAE-SUPAERO (FRANCE)
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This research benefited from the support of the ``FMJH Program Gaspard Monge in optimization and operation research'', and from the support to this program from EDF.
Deposited On:16 Feb 2018 14:28

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