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A Comparison of Three Implementations of Multi-Label Conformal Prediction

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Statistical Learning and Data Sciences (SLDS 2015)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 9047))

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Abstract

The property of calibration of Multi-Label Learning (MLL) has not been well studied. Because of the excellent calibration property of Conformal Predictors (CP), it is valuable to achieve calibrated MLL prediction via CP. Three practical implementations of Multi-Label Conformal Predictors (MLCP) can be established. Among them are Instance Reproduction MLCP (IR-MLCP), Binary Relevance MLCP (BR-MLCP) and Power Set MLCP (PS-MLCP). The experimental results on benchmark datasets show that all three MLCP methods possess calibration property. Comparatively speaking, BR-MLCP performs better in terms of prediction efficiency and computational cost than the other two.

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Authors and Affiliations

  1. College of Computer Science and Technology, Huaqiao University, Quanzhou, China

    Huazhen Wang & Xin Liu

  2. Computer Learning Research Centre, Royal Holloway, University of London, Egham, UK

    Huazhen Wang, Ilia Nouretdinov & Zhiyuan Luo

Authors
  1. Huazhen Wang
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  2. Xin Liu
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  3. Ilia Nouretdinov
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  4. Zhiyuan Luo
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Corresponding author

Correspondence to Huazhen Wang .

Editor information

Editors and Affiliations

  1. University of London, Egham, Surrey, United Kingdom

    Alexander Gammerman

  2. University of London, Egham, Surrey, United Kingdom

    Vladimir Vovk

  3. Frederick University, Nicosia, Cyprus

    Harris Papadopoulos

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© 2015 Springer International Publishing Switzerland

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Wang, H., Liu, X., Nouretdinov, I., Luo, Z. (2015). A Comparison of Three Implementations of Multi-Label Conformal Prediction. In: Gammerman, A., Vovk, V., Papadopoulos, H. (eds) Statistical Learning and Data Sciences. SLDS 2015. Lecture Notes in Computer Science(), vol 9047. Springer, Cham. https://doi.org/10.1007/978-3-319-17091-6_19

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  • DOI: https://doi.org/10.1007/978-3-319-17091-6_19

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17090-9

  • Online ISBN: 978-3-319-17091-6

  • eBook Packages: Computer ScienceComputer Science (R0)

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