A Novel Approach for Data Classification Using Neutrosophic Entropy

  • Kanika Bhutani
  • Swati Aggarwal
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 732)


Fuzzy classification is very necessary because it has the ability to use interpretable rules. It has got control over the limitations of crisp rule-based classification. This paper mainly deals with classification using fuzzy probability and Neutrosophic probability. Classification based on Neutrosophic probability employs Neutrosophic logic, Neutrosophic probability, and Neutrosophic entropy for its working and is compared with classification based on fuzzy probability on the basis of parameters such as probability and ambiguity in the results. Classification based on fuzzy and Neutrosophic probabilities is implemented on appendicitis dataset from knowledge extraction based on evolutionary learning.


Classification Fuzzy probability Fuzzy entropy Neutrosophic probability Neutrosophic entropy 


  1. 1.
    National Diabetes Data Group: Classification and diagnosis of diabetes mellitus and other categories of glucose intolerance. Diabetes 28(12), 1039–1057 (1979)CrossRefGoogle Scholar
  2. 2.
    Rahman, R.M., Afroz, F.: Comparison of various classification techniques using different data mining tools for diabetes diagnosis. J. Softw. Eng. Appl. 6(03), 85 (2013)CrossRefGoogle Scholar
  3. 3.
    Adlassnig, K.P.: Fuzzy set theory in medical diagnosis. IEEE Trans. Syst. Man Cybern. 16(2), 260–265 (1986)CrossRefGoogle Scholar
  4. 4.
    Zimmermann, H.J.: Fuzzy set theory. Wiley Interdisc. Rev. Comput. Stat. 2(3), 317–332 (2010)CrossRefGoogle Scholar
  5. 5.
    Zadeh, L.A.: Fuzzy probabilities. Inf. Proc. Manag. 20(3), 363–372 (1984)CrossRefGoogle Scholar
  6. 6.
    Pal, N.R., Bezdek, J.C.: Measuring fuzzy uncertainty. IEEE Trans. Fuzzy Syst. 2(2), 107–118 (1994)CrossRefGoogle Scholar
  7. 7.
    Kosko, B.: Fuzzy entropy and conditioning. Inf. Sci. 40(2), 165–174 (1986)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Smarandache, F.: Introduction to neutrosophic measure, neutrosophic integral, and neutrosophic probability (2013)Google Scholar
  9. 9.
    Majumdar, P., Samanta, S.K.: On similarity and entropy of neutrosophic sets. J. Intell. Fuzzy Syst. 26(3), 1245–1252 (2014)MathSciNetzbMATHGoogle Scholar
  10. 10.
    Retrieved appendicitis dataset on 10 Oct 2014 from
  11. 11.
    Smarandache, F.: Proceedings of the First International Conference on Neutrosophy, Neutrosophic Logic, Neutrosophic Set, Neutrosophic Probability and Statistics, University of New Mexico, Gallup Campus, Xiquan, Phoenix (2002)Google Scholar
  12. 12.
    Smarandache, F.: A unifying field in logics: neutrosophic logic. Multiple-Valued Logic/An Int. J. 8(3), 385–438 (2002)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Ansari, A.Q., Biswas, R., Aggarwal, S.: Neutrosophic classifier: an extension of fuzzy classifer. Appl. Softw. Comput. 13(1), 563–573 (2013)CrossRefGoogle Scholar
  14. 14.
    Şahin, R., Küçük, A.: On similarity and entropy of neutrosophic soft sets. J. Intell. Fuzzy Syst. Appl. Eng. Technol. 27(5), 2417–2430 (2014)MathSciNetzbMATHGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  1. 1.Department of Computer EngineeringNIT KurukshetraKurukshetraIndia
  2. 2.COENSITDwarkaIndia

Personalised recommendations