Classification Based on Possibilistic Likelihood

  • Mathieu Serrurier
  • Henri Prade
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 299)


Classification models usually associate one class for each new instance. This kind of prediction doesn’t reflect the uncertainty that is inherent in any machine learning algorithm. Probabilistic approaches rather focus on computing a probability distribution over the classes. However, making such a computation may be tricky and requires a large amount of data. In this paper, we propose a method based on the notion of possibilistic likelihood in order to learn a model that associates a possibility distribution over the classes to a new instance. Possibility distributions are here viewed as an upper bound of a family of probability distributions. This allows us to capture the epistemic uncertainty associated with the model in a faithful way. The model is based on a set of kernel functions and is obtained through an optimization process performed by a particle swarm algorithm. We experiment our method on benchmark dataset and compares it with a naive Bayes classifier.


Particle Swarm Optimization Benchmark Dataset Epistemic Uncertainty Possibility Distribution Possibility Theory 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Mathieu Serrurier
    • 1
  • Henri Prade
    • 1
  1. 1.IRITUniversité Paul SabatierToulouse Cedex 9France

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