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Maximising entropy on the nonparametric predictive inference model for multinomial data

Author

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  • Abellán, Joaquín
  • Baker, Rebecca M.
  • Coolen, Frank P.A.

Abstract

The combination of mathematical models and uncertainty measures can be applied in the area of data mining for diverse objectives with as final aim to support decision making. The maximum entropy function is an excellent measure of uncertainty when the information is represented by a mathematical model based on imprecise probabilities. In this paper, we present algorithms to obtain the maximum entropy value when the information available is represented by a new model based on imprecise probabilities: the nonparametric predictive inference model for multinomial data (NPI-M), which represents a type of entropy-linear program. To reduce the complexity of the model, we prove that the NPI-M lower and upper probabilities for any general event can be expressed as a combination of the lower and upper probabilities for the singleton events, and that this model can not be associated with a closed polyhedral set of probabilities. An algorithm to obtain the maximum entropy probability distribution on the set associated with NPI-M is presented. We also consider a model which uses the closed and convex set of probability distributions generated by the NPI-M singleton probabilities, a closed polyhedral set. We call this model A-NPI-M. A-NPI-M can be seen as an approximation of NPI-M, this approximation being simpler to use because it is not necessary to consider the set of constraints associated with the exact model.

Suggested Citation

  • Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A., 2011. "Maximising entropy on the nonparametric predictive inference model for multinomial data," European Journal of Operational Research, Elsevier, vol. 212(1), pages 112-122, July.
    • Handle: RePEc:eee:ejores:v:212:y:2011:i:1:p:112-122
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    References listed on IDEAS

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    1. Chateauneuf, Alain & Jaffray, Jean-Yves, 1989. "Some characterizations of lower probabilities and other monotone capacities through the use of Mobius inversion," Mathematical Social Sciences, Elsevier, vol. 17(3), pages 263-283, June.
    2. Abellán, Joaquín & Masegosa, Andrés R., 2010. "An ensemble method using credal decision trees," European Journal of Operational Research, Elsevier, vol. 205(1), pages 218-226, August.
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    Cited by:

    1. Houlding, B. & Coolen, F.P.A., 2012. "Nonparametric predictive utility inference," European Journal of Operational Research, Elsevier, vol. 221(1), pages 222-230.
    2. Frank PA Coolen & Tahani Coolen-Maturi & Abdullah H Al-nefaiee, 2014. "Nonparametric predictive inference for system reliability using the survival signature," Journal of Risk and Reliability, , vol. 228(5), pages 437-448, October.
    3. Abellán, Joaquín & Baker, Rebecca M. & Coolen, Frank P.A. & Crossman, Richard J. & Masegosa, Andrés R., 2014. "Classification with decision trees from a nonparametric predictive inference perspective," Computational Statistics & Data Analysis, Elsevier, vol. 71(C), pages 789-802.
    4. Aboalkhair, Ahmad M. & Coolen, Frank P.A. & MacPhee, Iain M., 2013. "Nonparametric predictive reliability of series of voting systems," European Journal of Operational Research, Elsevier, vol. 226(1), pages 77-84.
    5. Fernández, Arturo J., 2012. "Minimizing the area of a Pareto confidence region," European Journal of Operational Research, Elsevier, vol. 221(1), pages 205-212.

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