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A New Hybrid Possibilistic-Probabilistic Decision-Making Scheme for Classification.

Basel Solaiman1, Didier Guériot1, Shaban Almouahed1

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Summary

This study introduces a hybrid decision-making scheme for classification using both probabilistic (hard) and linguistic (soft) information. A new Possibilistic Maximum Likelihood (PML) criterion improves classification by integrating both uncertainty types without conversion.

Keywords:
Bayesian decisionimage processingmaximum a posterioripattern classificationpossibilistic decision rulepossibilistic maximum likelihoodpossibility theoryuncertainty

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Area of Science:

  • Decision-making under uncertainty
  • Information fusion
  • Machine learning

Background:

  • Real-world decision-making involves aleatory (probabilistic) and epistemic (possibilistic/linguistic) uncertainty.
  • Existing methods often convert one uncertainty type to another, potentially losing information.
  • Classification tasks frequently encounter mixed data sources.

Purpose of the Study:

  • To propose a novel hybrid decision-making scheme for classification.
  • To introduce a Possibilistic Maximum Likelihood (PML) criterion.
  • To enhance classification accuracy by jointly utilizing hard and soft information.

Main Methods:

  • Development of a hybrid classification framework.
  • Introduction of the Possibilistic Maximum Likelihood (PML) criterion.
  • Jointly processing probabilistic and possibilistic data within a probabilistic decision framework.

Main Results:

  • The proposed PML criterion improves classification rates compared to classical methods using only hard data.
  • The scheme effectively integrates both probabilistic and possibilistic information sources.
  • Avoids information loss associated with type conversion.

Conclusions:

  • The novel hybrid approach offers a robust method for classification with mixed uncertainty types.
  • PML provides a significant advancement in handling diverse information sources.
  • This framework enhances decision-making accuracy in complex, real-world scenarios.