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Efficient extreme classification

Mouhamadou Moustapha Cisse 1
1 MLIA - Machine Learning and Information Access
LIP6 - Laboratoire d'Informatique de Paris 6
Abstract : We propose in this thesis new methods to tackle classification problems with a large number of labes also called extreme classification. The proposed approaches aim at reducing the inference conplexity in comparison with the classical methods such as one-versus-rest in order to make learning machines usable in a real life scenario. We propose two types of methods respectively for single label and multilable classification. The first proposed approach uses existing hierarchical information among the categories in order to learn low dimensional binary representation of the categories. The second type of approaches, dedicated to multilabel problems, adapts the framework of Bloom Filters to represent subsets of labels with sparse low dimensional binary vectors. In both approaches, binary classifiers are learned to predict the new low dimensional representation of the categories and several algorithms are also proposed to recover the set of relevant labels. Large scale experiments validate the methods.
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Mouhamadou Moustapha Cisse. Efficient extreme classification. Data Structures and Algorithms [cs.DS]. Université Pierre et Marie Curie - Paris VI, 2014. English. ⟨NNT : 2014PA066594⟩. ⟨tel-01142046⟩

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