A Modified Naïve Bayes Style Possibilistic Classifier for the Diagnosis of Lymphatic Diseases.

In this paper, we propose a modified version of the Naive Bayes Style Possibilistic Classifier (NBSPC) which has been already suggested to make decision from the categorical and subjective medical information included by the lymphography dataset of University of California Irvine (UCI). As the former NBSPC, the modified classifier combines the structure of the Naive Bayes Classifier (NBC) as a good classifier for discrete features with the possibility theory as a powerful framework for belief estimation from subjective data. However, unlike the former NBSPC which uses the minimum as a fusion operator, the proposed classifier fuses possibilistic beliefs using the generalized minimum-based algorithm which has been recently proposed to deal with heterogeneous medical data. Experimental evaluations on the lymphograhy dataset show that the proposed G-Min-based NBSPC outperforms the former NBSPC as well as the main classification techniques which have been used in related work.