Interpreting Fuzzy Decision Trees with Probability-Possibility Mixtures - Intelligence Artificielle
Conference Papers Year : 2024

Interpreting Fuzzy Decision Trees with Probability-Possibility Mixtures

Abstract

The interpretation of the classification result obtained with a fuzzy decision tree is a not-so-easy task as the meaning of the obtained degrees of membership may depend on the type of fuzzy partition involved, ranging from probabilistic to possibilistic readings. Hybrid probabilistic-possibilistic mixtures can provide an interesting way to clarify the underlying components of such a classification result. In this paper, based on the hybrid-mixture model, a new approach is proposed to analyse the result of the classification with a fuzzy decision tree that enhance the explainability of this model.
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Dates and versions

hal-04670046 , version 1 (17-09-2024)

Identifiers

  • HAL Id : hal-04670046 , version 1

Cite

Didier Dubois, Romain Guillaume, Christophe Marsala, Agnès Rico. Interpreting Fuzzy Decision Trees with Probability-Possibility Mixtures. 20th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems (IPMU 2024), Instituto Superior Técnico, University of Lisbon, Jul 2024, Lisbon, Portugal. ⟨hal-04670046⟩
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