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International Conference on Discovery Science

DS 2018: Discovery Science pp 51–65Cite as

Feature Ranking with Relief for Multi-label Classification: Does Distance Matter?

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 11198))

Abstract

In this work, we address the task of feature ranking for multi-label classification (MLC). The task of MLC is to predict which labels from a maximal predefined label set are relevant for a given example. We focus on the Relief family of feature ranking algorithms and empirically show that the definition of the distances in the target space used within Relief should depend on the evaluation measure used to assess the performance of MLC algorithms. By considering different such measures, we improve over the currently available MLC Relief algorithm. We extensively evaluate the resulting MLC ranking approaches on 24 benchmark MLC datasets, using different evaluation measures of MLC performance. The results additionally identify the mechanisms of influence of the parameters of Relief on the quality of the rankings.

We acknowledge the financial supported of the Slovenian Research Agency via the grants P2-0103, J4-7362, L2-7509, N2-0056, and a young researcher grant to MP, the European Commission, through the grants HBP (The Human Brain Project), SGA2, and LANDMARK, and ARVALIS (project BIODIV).

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Author information

Authors and Affiliations

  1. Jožef Stefan Institute, Jamova 39, 1000, Ljubljana, Slovenia

    Matej Petković, Dragi Kocev & Sašo Džeroski

  2. Jožef Stefan Postgraduate School, Jamova 39, 1000, Ljubljana, Slovenia

    Matej Petković, Dragi Kocev & Sašo Džeroski

Authors
  1. Matej Petković
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  2. Dragi Kocev
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  3. Sašo Džeroski
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Corresponding author

Correspondence to Matej Petković .

Editor information

Editors and Affiliations

  1. Goldsmiths University of London, London, UK

    Larisa Soldatova

  2. Eindhoven University of Technology, Eindhoven, The Netherlands

    Joaquin Vanschoren

  3. University of Cyprus, Nicosia, Cyprus

    George Papadopoulos

  4. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

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Petković, M., Kocev, D., Džeroski, S. (2018). Feature Ranking with Relief for Multi-label Classification: Does Distance Matter?. In: Soldatova, L., Vanschoren, J., Papadopoulos, G., Ceci, M. (eds) Discovery Science. DS 2018. Lecture Notes in Computer Science(), vol 11198. Springer, Cham. https://doi.org/10.1007/978-3-030-01771-2_4

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  • DOI: https://doi.org/10.1007/978-3-030-01771-2_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01770-5

  • Online ISBN: 978-3-030-01771-2

  • eBook Packages: Computer ScienceComputer Science (R0)

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