Naive Bayes Classification for Subset Selection in a Multi-label Setting
Luca Mossina, Emmanuel Rachelson · Open Archive Toulouse Archive Ouverte (University of Toulouse) · 2018
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 \ bx algorithm, an extension of naive Bayes into the multi-label domain. Its properties are discussed and evaluated on real-world data.