A new possibilistic classifier for mixed categorical and numerical data based on a bi-module possibilistic estimation and the generalized minimum-based algorithm

Abstract

  In this paper, we suggest NPC _m , a new Naïve Bayesian-like Possibilistic Classifier for mixed categorical and numerical data. The proposed classifier is based on a bi-module belief estimation as well as the Generalized Minimum-based (G-Min) algorithm which has been recently proposed for the classification of categorical data. Distinctively, in the design of both categorical and numerical belief estimation modules, we make use of a probability-to-possibility transform-based possibilistic approach as a strong alternative to the probabilistic one when dealing with decision-making under uncertainty. Thereafter, we use the G-Min algorithm as an improvement of the minimum algorithm to make decision from possibilistic beliefs. Experimental evaluations on 12 datasets taken from University of California Irvine (UCI) and containing all mixed data, confirm the effectiveness of the proposed new G-Min-based NPC _m . Indeed, with the used datasets, the proposed classifier outperforms all the classical Bayesian-like classification methods. Consequently, we prove the efficient use of the bi-module possibilistic estimation approach together with the G-Min algorithm for the classification of mixed categorical and numerical data.