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A Neuro-Fuzzy Classification System Using Dynamic Clustering

  • Heisnam Rohen Singh
  • Saroj Kr Biswas
  • Biswajit Purkayastha
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

Classification task provides a deep insight into the data and helps in better understanding and effective decision-making. It is mostly associated with feature selection for better performance. Various techniques are used for classification; however, they provide poor explanation and understandability. Neuro-fuzzy techniques are most suitable for better understandability. In the neuro-fuzzy system, the features are interpreted with some linguistic form. In these existing neuro-fuzzy systems, numbers of linguistic variables are produced for each input. This leads to more computational, limited explanation, and understandability to the generated classification rules. In this, a neuro-fuzzy system is suggested for rule-based classification and the novelty lies in the way significant linguistic variables are generated, and it results in better transparency and accuracy of classification rules. The performance of the proposed system is tested with eight benchmark datasets from UCI repository.

Keywords

Classification Neural network Fuzzy logic Neuro-fuzzy system Clustering Linguistic variable selection 

References

  1. 1.
    Guyon, I., Elisseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3(1), 1157–1182 (2003)MATHGoogle Scholar
  2. 2.
    Chang, C., Verhaegen, P.A., Duflou, J.R.: A comparison of classifiers for intelligent machine usage prediction. Intell. Environ. (IE) 198–201 (2014)Google Scholar
  3. 3.
    Kohavi, R.: A study of cross-validation and bootstrap for accuracy estimation and model selection. Int. Joint Conf. Artif. Intell. (IJCAI) 1137–1145 (1995)Google Scholar
  4. 4.
    Puch, W., Goodman, E., Pei, M., Chia-Shun, L., Hovland, P., Enbody, R.: Further research on feature selection and classification using genetic algorithm. In: International Conference on Genetic algorithm, 557–564 (1993)Google Scholar
  5. 5.
    Inza, I., Larranaga, P., Sierra, B.: Feature selection by bayesian networks: a comparison with genetic and sequential algorithm. Approx. Reason. 27(2), 143–164 (2001)CrossRefGoogle Scholar
  6. 6.
    Ledesma, S., Cerda, G., Avina, G., Hernandez, D., Torres, M.: Feature selection using artificial neural networks. In MICAI 2008. Adv. Artif. Intell. 5317, 351–359 (2008)Google Scholar
  7. 7.
    Cover, T.M., Hart, P.E.: Nearest neighbor pattern classification. IEEE Trans. Inf. Theory 13(1) (1967)Google Scholar
  8. 8.
    Ngai, E.W.T., Xiu, L.: Chau, DCK.: Application of data mining techniques in customer relationship management: a literature review and classification. Expert Syst. Appl. 36(2), 2592–2602 (2009)CrossRefGoogle Scholar
  9. 9.
    Jang, J.R.: ANFIS: adaptive-network-based fuzzy inference system. IEEE Trans. Syst. Man Cybern. 23(3), 665–685 (1993)CrossRefGoogle Scholar
  10. 10.
    Nauck, D.: Neuro-fuzzy classification studies in classification. Data Anal. Knowl. Organ. 287–294 (1998)Google Scholar
  11. 11.
    De, R.K., Basak, J., Pal, S.K.: Neuro-fuzzy feature evaluation with theoretical analysis. Neural Netw. 12(10), 1429–1455 (1999)CrossRefGoogle Scholar
  12. 12.
    Li, R.P., Mukaidono, M., Turksen, I.B.: A fuzzy neural network for pattern classification and feature selection. Fuzzy Sets Syst. 130(1), 101–108 (2002)MathSciNetCrossRefGoogle Scholar
  13. 13.
    Yang, J.Y., Chen, Y.P, Lee, G.Y., Liou, S.N., Wang, J.S.: Activity recognition using one triaxial accelerometer: a neuro-fuzzy classifier with feature reduction. Entertain. Comput. ICEC. 4740, 395–400 (2007)Google Scholar
  14. 14.
    Chen, C.H., Lin, C.J., Lin, C.T.: An efficient quantum neuro-fuzzy classifier based on fuzzy entropy and compensatory operation. Soft. Comput. 12(6), 567–583 (2008)CrossRefGoogle Scholar
  15. 15.
    Ghosh, A., Shankar, B.U., Meher, S.K.: A novel approach to neuro-fuzzy classification. Neural Networks. 22, 100–109 (2009)CrossRefGoogle Scholar
  16. 16.
    Ghosh, S., Biswas, S., Sarkar, D., Sarkar, P.P.: A novel Neuro-fuzzy classification technique for data mining. Egypt. Inf. J. 15(3), 129–147 (2014)CrossRefGoogle Scholar
  17. 17.
    Cetisli, B.: Development of an adaptive neuro-fuzzy classifier using linguistic hedges: Part 1. Expert Syst. Appl. 37, 6093–6101 (2010)CrossRefGoogle Scholar
  18. 18.
    Cetisli, B.: The effect of linguistic hedges on feature selection: Part 2. Expert Syst. Appl. 37, 6102–6108 (2010)CrossRefGoogle Scholar
  19. 19.
    Azar, A.T., Hassanien, A.E.: Dimensionality reduction of medical big data using neural-fuzzy classifier. Soft. Comput. 19(4), 1115–1127 (2015)CrossRefGoogle Scholar
  20. 20.
    Chakraborty, D., Pal, N.R.: designing rule-based classifiers with on-line feature selection: a neuro-fuzzy approach. In: Pal, N.R., Sugeno, M. (eds.), AFSS. LNAI, vol. 2275, 251–259 (2002)Google Scholar
  21. 21.
    Sen, S., Pal, T.: A neuro-fuzzy scheme for integrated input fuzzy set selection and optimal fuzzy rule generation for classification. Premi LNCS 4815, 287–294 (2007)Google Scholar
  22. 22.
    Eiamkanitchat, N., Umpon, T., Sansanee, A.: A novel neuro-fuzzy method for linguistic feature selection and rule-based classification. In: Proceedings of the in 2nd International Conference on Computer and Automation Engineering (ICCAE), pp. 247–252 (2010)Google Scholar
  23. 23.
    Eiamkanitchat, N., Umpon, T.: Colon tumor microarray classification using neural network with feature selection and rule based classification. Adv. Neural Netw. Res. Appl. 67, 363–372 (2010)CrossRefGoogle Scholar
  24. 24.
    Biswas, S.K., Bordoloi, M., Singh, H.R., Purkayasthaya, B.: A neuro-fuzzy rule-based classifier using important features and top linguistic features. Int. J. Intell. Inf. Technol. (IJIIT) 12(3), 38–50 (2016)CrossRefGoogle Scholar
  25. 25.
    Wongchomphu, P., Eiamkanitchat, N.: Enhance neuro-fuzzy system for classification using dynamic clustering. In: Proceedings of the in 4th Joint International Conference on Information and Communication Technology, Electronic and Electrical Engineering (JICTEE), pp.1–6 (2014)Google Scholar
  26. 26.
    Napook, P., Eiamkanitchat, E.: The adaptive dynamic clustering neuro-fuzzy system for classification. Inf. Sci. Appl. 339, 721–728 (2015)Google Scholar
  27. 27.
    Jin, Y.: Fuzzy modeling of high-dimensional systems: complexity reduction and interpretability improvement. IEEE Trans. Fuzzy Syst. 8(2), 212–221 (2000)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Heisnam Rohen Singh
    • 1
  • Saroj Kr Biswas
    • 1
  • Biswajit Purkayastha
    • 1
  1. 1.NITSilcharIndia

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