Affiliations: [a] REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia
| [b] Esprit School of Engineering, Tunis, Tunisia
| [c] Taibah University, College Of Science and arts at Al-Ula, Al-Madinah al-Munawwarah, KSA | [d] Machines Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, WA, USA
Correspondence:
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Corresponding author. Karim Baati. REGIM-Lab.: REsearch Groups on Intelligent Machines, University of Sfax, National Engineering School of Sfax (ENIS), Sfax, Tunisia; Esprit School of Engineering, Tunis, Tunisia; E-mail: [email protected].
Abstract: In this paper, we propose NPCc, a new Naïve Possibilistic Classifier for categorical data. The proposed classifier relies on the Bayesian structure of the Naïve Bayes Classifier for categorical data (NBCc) which stands for an interesting pattern when dealing with discrete attributes. However, unlike NBCc, the proposed NPCc is based on the possibilistic formalism as an efficient fuzzy-sets-based alternative to the probabilistic one when handling uncertain data. Distinctively, we use the possibilistic approach to estimate beliefs from categorical data and a Generalized Minimum-based classification algorithm (G-Min) as a novel algorithm to make decision from possibilistic beliefs. Experimental evaluations on 12 datasets taken from University of California Irvine (UCI) and containing all categorical data, confirm the effectiveness of the proposed new G-Min-based NPCc. With the used datasets, the proposed classifier outperforms the commonly-used classifiers for categorical data including NBCc, C4.5-based decision tree and RIPPER-based classifier. Moreover, it outperforms the two versions of NPCc using commonly-used possibilistic classification algorithms which are based on respectively, the product and the minimum operators. Consequently, we prove the efficiency of the possibilistic approach together with the G-Min algorithm for the classification of categorical data.
