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Analyzing Classification Methods in Multi-label Tasks

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Artificial Neural Networks – ICANN 2010 (ICANN 2010)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 6354))

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Abstract

Multi-label classification methods have been increasingly used in modern application, such as music categorization, functional genomics and semantic annotation of images. This paper presents a comparative analysis of some existing multi-label classification methods applied to different domains. The main aim of this analysis is to evaluate the performance of such methods in different tasks and using different evaluation metrics.

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Author information

Authors and Affiliations

  1. Campus Angicos, Federal Rural University of Semi-Árido (UFERSA), Angicos, RN, Brazil, 59515-000

    Araken M. Santos

  2. Informatics and Applied Mathematics Department, Federal University of Rio Grande do Norte (UFRN), Natal, RN, Brazil, 59072-970

    Laura E. A. Santana & Anne M. Canuto

Authors
  1. Araken M. Santos
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  2. Laura E. A. Santana
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  3. Anne M. Canuto
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Editor information

Editors and Affiliations

  1. Department of Informatics, TEI of Thessaloniki, 57400, Sindos, Greece

    Konstantinos Diamantaras

  2. Department of Informatics, Nicolaus Copernicus University, School of Physics, Astronomy, and Informatics, ul. Grudziadzka 5, 87-100, Torun, Poland

    Wlodek Duch

  3. Department of Forestry and Management of the Environment and Natural Resources, Democritus University of Thrace, Pantazidou 193, 68200, Orestiada Thrace, Greece

    Lazaros S. Iliadis

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© 2010 Springer-Verlag Berlin Heidelberg

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Santos, A.M., Santana, L.E.A., Canuto, A.M. (2010). Analyzing Classification Methods in Multi-label Tasks. In: Diamantaras, K., Duch, W., Iliadis, L.S. (eds) Artificial Neural Networks – ICANN 2010. ICANN 2010. Lecture Notes in Computer Science, vol 6354. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15825-4_18

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  • DOI: https://doi.org/10.1007/978-3-642-15825-4_18

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15824-7

  • Online ISBN: 978-3-642-15825-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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