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Decision Quality Enhancement in Minimum-Based Possibilistic Classification 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 614)

Abstract

Two Bayesian-like possibilistic classifiers based on the transformation of Dubois et al. in the continuous case have been proposed to deal with numerical data. For these two classifiers, namely Naïve Possibilistic Classifier (NPC) and Flexible Naïve Possibilistic Classifier (FNPC), the minimum operator has led to less accurate classification when compared to the one produced by the product rule. In this paper, we investigate the use of the Generalized Minimum-based (G-Min) algorithm that has been recently suggested as an alternative to the minimum operator for combining possibilistic estimates. The main objective is to enhance the quality of decision within minimum-based possibilistic classifiers for numerical data. Experimental evaluations are conducted on 15 numerical datasets taken from University of California Irvine (UCI) and show that using the G-Min algorithm largely improves the classification accuracy within minimum-based NPC as well as minimum-based FNPC.

Keywords

Naïve Possibilistic Classifier Flexible Naïve Possibilistic Classifier Minimum operator G-Min algorithm Numerical data 

Notes

Aknowledgment

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.

References

  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. Reliab. 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.
    Baati, K., Hamdani, T.M., Alimi, A.M., Abraham, A.: A modified Naïve possibilistic classifier for numerical data. In: Proceedings of the 16th International Conference on Intelligent Systems Design and Applications. Springer (2016)Google Scholar
  6. 6.
    Bounhas, M., Mellouli, K., Prade, H., Serrurier, M.: Possibilistic classifiers for numerical data. Soft. Comput. 17, 733–751 (2013)CrossRefzbMATHGoogle Scholar
  7. 7.
    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
  8. 8.
    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
  9. 9.
    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
  10. 10.
    Haouari, B., Ben Amor, N., Elouedi, Z., Mellouli, K.: Naïve possibilistic network classifiers. Fuzzy Sets Syst. 160(22), 3224–3238 (2009)CrossRefzbMATHGoogle Scholar
  11. 11.
    Benferhat, S., Tabia, K.: An efficient algorithm for Naïve possibilistic classifiers with uncertain inputs. In: Proceedings of the 2nd International Conference on Scalable Uncertainty Management (SUM). LNAI, pp. 63–77. Springer (2008)Google Scholar
  12. 12.
    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
  13. 13.
    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
  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 Naïve 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
  18. 18.
    Geiger, D., Heckerman, D.: Learning Gaussian networks. In: Proceedings of the Tenth International Conference on Uncertainty in Artificial Intelligence, pp. 235–243. Morgan Kaufmann Publishers Inc. (1994)Google Scholar
  19. 19.
    John, G.H., Langley, P.: Estimating continuous distributions in Bayesian classifiers. In: Proceedings of the 11th Conference on Uncertainty in Artificial Intelligence, pp. 338–345. Morgan Kaufmann Publishers Inc. (1995)Google Scholar

Copyright information

© Springer International Publishing AG 2018

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 Machines, National Engineering School of Sfax (ENIS)University of SfaxSfaxTunisia
  2. 2.Esprit School of EngineeringTunisTunisia
  3. 3.College of Science and Arts at Al-UlaTaibah UniversityMedinaSaudi Arabia
  4. 4.Machines Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research ExcellenceAuburnUSA

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