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A Modified Naïve Possibilistic Classifier for Numerical Data

  • Karim BaatiEmail author
  • Tarek M. Hamdani
  • Adel M. Alimi
  • Ajith Abraham
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 557)

Abstract

In this paper, we propose a modified version of the Naïve Possibilistic Classifier (NPC) which has been already suggested to make decision from numerical data. As the former NPC, the modified classifier makes use of the probability to possibility transformation of Dubois et al. in the continuous case in order to estimate possibilistic beliefs. However, unlike the former NPC which uses the product as a fusion operator, the proposed classifier fuses possibilistic beliefs using the generalized minimum-based algorithm which has been recently proposed as an improvement of the minimum operator for combining possibilistic estimates. Experimental evaluations are conducted on 15 numerical datasets taken from University of California Irvine (UCI) and show that the new version of NPC largely outperforms the former one in terms of accuracy.

Keywords

Naïve possibilistic classifier Possibility theory G-Min algorithm Numerical data 

Notes

Acknowledgment

The authors would like to acknowledge the financial support of this work by grants from General Direction of Scientific Research (DGRST), Tunisia, under the ARUB program.

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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Karim Baati
    • 1
    • 2
    Email author
  • Tarek M. Hamdani
    • 1
    • 3
  • Adel M. Alimi
    • 1
  • Ajith Abraham
    • 4
  1. 1.REGIM-Lab.: REsearch Groups on Intelligent MachinesUniversity of Sfax, National Engineering School of Sfax (ENIS)SfaxTunisia
  2. 2.Esprit School of EngineeringTunisTunisia
  3. 3.Taibah University, College of Science And Arts at Al-UlaAl-madinah Al-munawwarahKingdom of Saudi Arabia
  4. 4.Machines Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research ExcellenceAuburnUSA

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