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Renforcement de l'Apprentissage Structurel pour la reconnaissance du Diabète

Abstract : Interpretability represents the most important driving force behind the implementation of fuzzy-based classifiers for medical application problems. The expert should be able to understand the classifier and to evaluate its results. Fuzzy rule based models are especially suitable, because they consist of simple linguistically interpretable rules. The majority of classifiers based on an adaptive neuro-fuzzy inference system (ANFIS) used in literature do not provide enough explanation of how their inference results have been obtained. This Magister thesis discusses the possibility to increase the interpretability of ANFIS classifier by the hybridation with the clustering method Fuzzy C means. It is shown how a readable neuro-fuzzy classifier can be obtained by a learning process and how fuzzy rules extracted can enhance its interpretability. Experimental results show that the proposed fuzzy classifier can achieve a good tradeoff between the accuracy and interpretability. Also the basic structure of the fuzzy rules which were automatically extracted from the UCI Machine learning database shows strong similarities to the rules applied by human experts. Results are compared to other approaches in the literature. The proposed approach gives more compact, interpretable and accurate classifier.
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Submitted on : Saturday, July 2, 2011 - 10:34:07 PM
Last modification on : Friday, September 16, 2016 - 3:07:34 PM
Long-term archiving on: : Monday, October 3, 2011 - 2:21:52 AM


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  • HAL Id : inria-00605627, version 1



Nesma Settouti. Renforcement de l'Apprentissage Structurel pour la reconnaissance du Diabète. [Rapport de recherche] 2011, pp.42. ⟨inria-00605627⟩



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