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)


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.


Naïve possibilistic classifier Possibility theory G-Min algorithm Numerical data 



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.


  1. 1.
    Langley, P., Iba, W., Thompson, K.: An analysis of Bayesian classifiers. In: Proceedings of AAAI, pp. 223–228 (1992)Google Scholar
  2. 2.
    Khaleghi, B., Khamis, A., Karray, F.O., Razavi, S.N.: Multisensor data fusion: a review of the state-of-the-art. Inf. Fusion 14(1), 28–44 (2013)CrossRefGoogle Scholar
  3. 3.
    Dubois, D., Foulloy, L., Mauris, G., Prade, H.: Probability-possibility transformations, triangular fuzzy sets, and probabilistic inequalities. Reliable Comput. 10(4), 273–297 (2004)MathSciNetCrossRefzbMATHGoogle Scholar
  4. 4.
    Baati, K., Kanoun, S., Benjlaiel, M.: Différenciation d’écritures arabe et latine de natures imprime et manuscrite par approche globale. In: Colloque International Francophone Sur l’Ecrit et le Document (CIFED) (2010)Google Scholar
  5. 5.
    Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft Comput. 17, 733–751 (2013)CrossRefzbMATHGoogle Scholar
  6. 6.
    Baati, K., Hamdani, T.M., Alimi, A.M.: Diagnosis of lymphatic diseases using a naïve bayes style possibilistic classifier. In: Proceedings of the IEEE International Conference on Systems, Man and Cybernetics (SMC), pp. 4539–4542. IEEE (2013)Google Scholar
  7. 7.
    Baati, K., Hamdani, T.M., Alimi, A.M., Abraham, A.: A Modified Naïve Bayes Style Possibilistic Classifier for the Diagnosis of Lymphatic Diseases. In: Proceedings of the 16th International Conference on Hybrid Intelligent Systems. Springer (2016)Google Scholar
  8. 8.
    Baati, K., Hamdani, T.M., Alimi, A.M., Abraham, A.: A new possibilistic classifier for heart disease detection from heterogeneous medical data. Int. J. Comput. Sci. Inf. Secur. 14(7), 443–450 (2016)Google Scholar
  9. 9.
    Borgelt, C., Gebhardt, J.: A naïve bayes style possibilistic classifier. In: Proceedings of the 7th European Congress on Intelligent Techniques and Soft Computing (1999)Google Scholar
  10. 10.
    Borgelt, C., Kruse, R.: Efficient maximum projection of database induced multivariate possibility distributions. In: Proceedings of the 7th IEEE International Conference on Fuzzy Systems, pp. 663–668 (1988)Google Scholar
  11. 11.
    Haouari, B., Ben Amor, N., Elouedi, Z., Mellouli, K.: Naïve possibilistic network classifiers. Fuzzy Sets Syst. 160(22), 3224–3238 (2009)CrossRefzbMATHGoogle Scholar
  12. 12.
    Benferhat, S., Tabia, K.: An efficient algorithm for naive possibilistic classifiers with uncertain inputs. In: Greco, S., Lukasiewicz, T. (eds.) SUM 2008. LNCS (LNAI), vol. 5291, pp. 63–77. Springer, Heidelberg (2008). doi: 10.1007/978-3-540-87993-0_7 CrossRefGoogle Scholar
  13. 13.
    Bounhas, M., Hamed, M.G., Prade, H., Serrurier, M., Mellouli, K.: Naïve possibilistic classifiers for imprecise or uncertain numerical data. Fuzzy Sets Syst. 239, 137–156 (2014)CrossRefzbMATHGoogle Scholar
  14. 14.
    Zadeh, L.A.: Fuzzy sets as a basis for a theory of possibility. Fuzzy Sets Syst. 1(1), 3–28 (1978)MathSciNetCrossRefzbMATHGoogle Scholar
  15. 15.
    Dubois, D., Prade, H.M., Farreny, H., Martin-Clouaire, R., Testemale, C.: Possibility Theory: An Approach to Computerized Processing of Uncertainty 2. Plenum Press, New York (1988)CrossRefGoogle Scholar
  16. 16.
    Baati, K., Hamdani, T.M., Alimi, A.M.: Hybrid naïve possibilistic classifier for heart disease detection from heterogeneous medical data. In: Proceedings of the 13th International Conference on Hybrid Intelligent Systems, pp. 235–240. IEEE (2013)Google Scholar
  17. 17.
    Baati, K., Hamdani, T.M., Alimi, A.M.: A modified hybrid naive possibilistic classifier for heart disease detection from heterogeneous medical data. In: Proceedings of the 6th International Conference on Soft Computing and Pattern Recognition, pp. 353–35. IEEE (2014)Google Scholar

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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