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Categorizing feature selection methods for multi-label classification

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Abstract

In many important application domains such as text categorization, biomolecular analysis, scene classification and medical diagnosis, examples are naturally associated with more than one class label, giving rise to multi-label classification problems. This fact has led, in recent years, to a substantial amount of research on feature selection methods that allow the identification of relevant and informative features for multi-label classification. However, the methods proposed for this task are scattered in the literature, with no common framework to describe them and to allow an objective comparison. Here, we revisit a categorization of existing multi-label classification methods and, as our main contribution, we provide a comprehensive survey and novel categorization of the feature selection techniques that have been created for the multi-label classification setting. We conclude this work with concrete suggestions for future research in multi-label feature selection which have been derived from our categorization and analysis.

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Authors and Affiliations

  1. Universidade Federal Fluminense (UFF), Niterói, RJ, Brazil

    Rafael B. Pereira & Alexandre Plastino

  2. IBM Research - Brazil, Rio de Janeiro, RJ, Brazil

    Bianca Zadrozny

  3. Universidade Federal de Ouro Preto (UFOP), Ouro Preto, MG, Brazil

    Luiz H. C. Merschmann

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  1. Rafael B. Pereira
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  2. Alexandre Plastino
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  3. Bianca Zadrozny
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  4. Luiz H. C. Merschmann
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Correspondence to Rafael B. Pereira.

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Pereira, R.B., Plastino, A., Zadrozny, B. et al. Categorizing feature selection methods for multi-label classification. Artif Intell Rev 49, 57–78 (2018). https://doi.org/10.1007/s10462-016-9516-4

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