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The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems

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Econometrics of Risk

Part of the book series: Studies in Computational Intelligence ((SCI,volume 583))

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Abstract

Multi-label classification can be applied to study empirically discrete choice problems, in which each individual chooses more than one alternative. We applied the Classifier Chain (CC) method to transform the Generalized Maximum Entropy (GME) choice model from a single-label model to a multi-label model. The contribution of our CC-GME model lies in the advantages of both the GME and CC models. Specifically, the GME model can not only predict each individual’s choice, but also robustly estimate model parameters that describe factors determining his or her choices. The CC model is a problem transformation method that allows the decision on each alternative to be correlated. We used Monte-Carlo simulations and occupational hazard data to compare the CC-GME model with other selected methodologies for multi-label problems using the Hamming Loss, Accuracy, Precision and Recall measures. The results confirm the robustness of GME estimates with respect to relevant parameters regardless of the true error distributions. Moreover, the CC method outperforms other methods, indicating that the incorporation of the information on dependence patterns among alternatives can improve prediction performance.

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Acknowledgments

We are highly appreciated and would like to acknowledge the financial support from the Thailand Research Fund through the Royal Golden Jubilee Ph.D. Program (Grant No. PHD/0211/2556).

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Authors and Affiliations

  1. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Supanika Leurcharusmee, Jirakom Sirisrisakulchai & Songsak Sriboonchitta

  2. Université de Technologie de Compiègne CNRS, UNR 7253 Heudiasyc, cedex, France

    Thierry Denœux

Authors
  1. Supanika Leurcharusmee
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  2. Jirakom Sirisrisakulchai
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  3. Songsak Sriboonchitta
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  4. Thierry Denœux
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Corresponding author

Correspondence to Supanika Leurcharusmee .

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Editors and Affiliations

  1. Japan Advanced Institute of Science and Technology, Nomi, Japan

    Van-Nam Huynh

  2. Department of Computer Science, University of Texas at El Paso, El Paso, Texas, USA

    Vladik Kreinovich

  3. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Songsak Sriboonchitta

  4. Faculty of Economics, Chiang Mai University, Chiang Mai, Thailand

    Komsan Suriya

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Leurcharusmee, S., Sirisrisakulchai, J., Sriboonchitta, S., Denœux, T. (2015). The Classifier Chain Generalized Maximum Entropy Model for Multi-label Choice Problems. In: Huynh, VN., Kreinovich, V., Sriboonchitta, S., Suriya, K. (eds) Econometrics of Risk. Studies in Computational Intelligence, vol 583. Springer, Cham. https://doi.org/10.1007/978-3-319-13449-9_13

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  • DOI: https://doi.org/10.1007/978-3-319-13449-9_13

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-13448-2

  • Online ISBN: 978-3-319-13449-9

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