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NRCS: Neutrosophic Rule-Based Classification System

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Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016 (IntelliSys 2016)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 15))

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Abstract

This article presents a Neutrosophic Rule-Based Classification System where neutrosophic logic (NL) is used to represent several forms of knowledge. The presented system generalizes the fuzzy rule-based classification by describing every logical variable with its truth, indeterminacy, and falsity degrees. Which are obtained from truth, indeterminacy, and falsity membership functions extracted from the fuzzy trapezoidal membership function. Then, it is followed by an extraction of the “IF-THEN” rules which used in the classification phase. Tests on different datasets; the Iris, the Wine, and the Wisconsin Diagnostic Breast Cancer (WDBC) have been done on the proposed neutrosophic rule based classification system. Comparisons between the proposed system and the fuzzy rule based classification system are done. The proposed system showed an accuracy of 94.7% compared with 89.5% of the fuzzy rule-based classification system, on average.

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References

  1. Hassanien, A., Mahmood, M., El-bendary, N., Hefny, H.: Fuzzy rule generation approach to granular computing using rough mereology. In: The 5th International Conference on Computer Research and Development (ICCRD 2013), 23–24 February 2013. 6 pages

    Google Scholar 

  2. Ansari, A., Biswas, R., Aggarwal, S.: Neutrosophic classifier: an extension of fuzzy classifer. Appl. Soft Comput. 13(1), 563–573 (2013)

    Article  Google Scholar 

  3. Salama, A., Alblowi, S.: Generalized neutrosophic set and generalized neutrosophic spaces. J. Comput. Sci. Eng. 2(7), 129–132 (2012)

    Article  Google Scholar 

  4. Ashbacher, C.: Introduction to Neutrosophic Logic. American Research Press, Rehoboth (2002)

    Google Scholar 

  5. Cordn, O.: Genetic fuzzy systems: evolutionary tuning and learning of fuzzy knowledge bases. In: Advances in Fuzzy Systems-Applications and Theory Series. WSPC, vol. 19 (2002)

    Google Scholar 

  6. Cordn, O., Herrera, F.: A proposal for improving the accuracy of linguistic modeling. IEEE Trans. Fuzzy Syst. 8, 335–344 (2000)

    Article  Google Scholar 

  7. Herrera, F., Cordn, O., Peregrn, A.: Applicability of the fuzzy operators in the design of fuzzy logic controllers. Fuzzy Sets Syst. 86, 15–41 (1997)

    Article  Google Scholar 

  8. Ishibuchi, H., Nozaki, K., Tanaka, H.: A simple but powerful heuristic method for generating fuzzy rules from numerical data. Fuzzy Sets Syst. 86(3), 251–270 (1997)

    Article  Google Scholar 

  9. Wang, H., Smarandache, F., Sunderraman, R., Zhang, Y.: Interval Neutrosophic Sets and Logic: Theory and Applications in Computing, 1st edn. Hexis, Neutrosophic Book Series (2005)

    Google Scholar 

  10. Hanafy, I., Salama, A., Mahfouz, K.: Correlation of neutrosophic data. Int. Refereed J. Eng. Sci. (IRJES) 1(2), 33–39 (2012)

    Google Scholar 

  11. Hanafy, I., Salama, A., Mahfouz, K.: Neutrosophic classical events and its probability. Int. J. Math. Comput. Appl. Res. (IJMCAR) 3(1), 171–178 (2013)

    Google Scholar 

  12. Jian, S., Lin, Y.: Hybrid Rough Sets and Applications in Uncertain Decision-Making, 1st edn. Auerbach Publications (2010)

    Google Scholar 

  13. Mankad, K., Sajja, P., Akerkar, R.: Evolving rules using genetic fuzzy approach-an educational case study. Int. J. Soft Comput. (IJSC) 2(1), 35–46 (2011)

    Article  Google Scholar 

  14. Robinson, A.: Non-standard Analysis. Princeton Landmarks in Mathematics and Physics Series. Princeton University Press, Princeton (1996)

    Google Scholar 

  15. Chen, R., Hu, Y.C., Tzeng, G.H.: Finding fuzzy classification rules using data mining techniques. Pattern Recogn. Lett. 24, 509–519 (2003)

    Article  MATH  Google Scholar 

  16. Alblowi, S., Salama, A., Eisa, M.: New concepts of neutrosophic sets. Int. J. Math. Comput. Appl. Res. (IJMCAR) 4(1), 59–66 (2014)

    Google Scholar 

  17. Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158, 2134–2152 (2007)

    Article  MathSciNet  MATH  Google Scholar 

  18. Smarandache, F.: Neutrosophic set, a generialization of the intuituionistics fuzzy sets. Int. J. Pure Appl. Math. 24, 287–297 (2005)

    MathSciNet  Google Scholar 

  19. Smarandache, F.: A Unifying Field in Logics: Neutrosophic Logic. Neutrosophy, Neutrosophic Set, Neutrosophic Probability, 3rd edn. American Research Press (2003)

    Google Scholar 

  20. Zadeh, L.: Fuzzy sets. Inf. Control 8, 338–353 (1965)

    Article  MATH  Google Scholar 

  21. Zhou, E., Khotanzad, A.: Fuzzy classifier design using genetic algorithms. Pattern Recogn. 40, 3401–3414 (2007)

    Article  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Faculty of Science, Cairo University, Giza, Egypt

    Sameh H. Basha & Areeg S. Abdalla

  2. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

  3. Scientific Research Group in Egypt (SRGE), Cairo, Egypt

    Sameh H. Basha & Aboul Ella Hassanien

Authors
  1. Sameh H. Basha
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  2. Areeg S. Abdalla
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  3. Aboul Ella Hassanien
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Corresponding author

Correspondence to Sameh H. Basha .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Engineering, School of Computing and Mathematics, University of Ulster at Jordanstown, Newtownabbey, United Kingdom

    Yaxin Bi

  2. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Supriya Kapoor

  3. The Science and Information (SAI) Organization, Bradford, West Yorkshire, United Kingdom

    Rahul Bhatia

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Basha, S.H., Abdalla, A.S., Hassanien, A.E. (2018). NRCS: Neutrosophic Rule-Based Classification System. In: Bi, Y., Kapoor, S., Bhatia, R. (eds) Proceedings of SAI Intelligent Systems Conference (IntelliSys) 2016. IntelliSys 2016. Lecture Notes in Networks and Systems, vol 15. Springer, Cham. https://doi.org/10.1007/978-3-319-56994-9_42

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  • DOI: https://doi.org/10.1007/978-3-319-56994-9_42

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-56993-2

  • Online ISBN: 978-3-319-56994-9

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