Diagnosis of Lymphatic Diseases Using a Naive Bayes Style Possibilistic Classifier

This paper investigates a Naïve Bayes Style Possibilistic Classifier (NBSPC) to make decision from the categorical and subjective medical information included by the lymphography dataset of University of California Irvine (UCI). Main focus of the work is to improve the classification accuracy. NBSPC simultaneously relies on the structure of the Naïve Bayes classifier as a good classifier for categorical features, and on the possibility theory as an interesting framework to model and fuse subjective medical data. Possibilistic measures are estimated within the NBSPC using maximum likelihood estimation and then the probability-possibility transformation method of Dubois et al. Results show that the proposed classifier outperforms other classification techniques which have been already evaluated on the same data.