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Fuzzy Design of Nearest Prototype Classifier

  • Yanela Rodríguez Alvarez
  • Rafael Bello Pérez
  • Yailé Caballero Mota
  • Yaima Filiberto Cabrera
  • Yumilka Fernández Hernández
  • Mabel Frias Dominguez
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11288)

Abstract

In pattern classification problems, many works have been carried out with the aim of designing good classifiers from different perspectives. These works achieve very good results in many domains. However, in general they are very dependent on some crucial parameters involved in the design. An alternative is to use fuzzy relations to eliminate thresholds and make the development of classifiers more flexible. In this paper, a new method for solving data classification problems based on prototypes is proposed. Using fuzzy similarity relations for the granulation of the universe, similarity classes are generated and a prototype is built for each similarity class. In the new approach we replace the relation of similarity between two objects by a binary fuzzy relation, which quantifies the strength of the relationship in a range of [0; 1]. Experimental results show that the performance of our method is superior to other methods.

Keywords

Prototype generation Similarity relations Fuzzy-rough sets theory Classification 

Notes

Acknowledgment

This research has been partially sponsored by VLIR-UOS Network University Cooperation Programme - Cuba.

References

  1. 1.
    Feng, F., et al.: Soft sets combined with fuzzy sets and rough sets: a tentative approach. Soft. Comput. 14(9), 899–911 (2010)CrossRefGoogle Scholar
  2. 2.
    Lin, T.Y., Cercone, N.: Rough Sets and Data Mining: Analysis of Imprecise Data. Springer, New York (2012).  https://doi.org/10.1007/978-1-4613-1461-5CrossRefGoogle Scholar
  3. 3.
    Ziarko, W.P., Sets, R.: Fuzzy Sets and Knowledge Discovery. Springer, London (2012)Google Scholar
  4. 4.
    Yao, Y.: Combination of rough and fuzzy sets based on α-level sets. In: Lin, T.Y., Cercone, N. (eds.) Rough sets and Data Mining, pp. 301–321. Springer, Boston (1997).  https://doi.org/10.1007/978-1-4613-1461-5_15CrossRefGoogle Scholar
  5. 5.
    Tsang, E.C., et al.: Hybridization of fuzzy and rough sets: present and future. In: Bustince, H., Herrera, F., Montero, J. (eds.) Fuzzy Sets and Their Extensions: Representation Aggregation and Models, pp. 45–64. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-73723-0_3CrossRefGoogle Scholar
  6. 6.
    Dubois, D., Prade, H.: Rough fuzzy sets and fuzzy rough sets. Int. J. Gener. Syst. 17(2–3), 191–209 (1990)CrossRefGoogle Scholar
  7. 7.
    Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Słowiński, R. (ed.) Intelligent Decision Support, pp. 203–232. Springer, Dordrecht (1992).  https://doi.org/10.1007/978-94-015-7975-9_14CrossRefGoogle Scholar
  8. 8.
    Radzikowska, A.M., Kerre, E.E.: A comparative study of fuzzy rough sets. Fuzzy Sets Syst. 126(2), 137–155 (2002)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Fernández Hernández, Y.B., et al.: An approach for prototype generation based on similarity relations for problems of classification. Computación y Sistemas 19(1), 109–118 (2015)CrossRefGoogle Scholar
  10. 10.
    Rodríguez, Y., et al., An Approach to solve classification problems on domains with hubness using rough sets and Nearest Prototype. In: 16th Mexican International Conference on Artificial Intelligence. Springer, Ensenada (2017)Google Scholar
  11. 11.
    Rodríguez, Y., et al.: Similar prototype methods for class imbalanced data classification. In: 2nd International Symposium on Fuzzy and Rough Sets (ISFUROS 2017), Varadero, Cuba, 24–26 October 2017. Springer, Heidelberg (2017)Google Scholar
  12. 12.
    Yao, Y.: Granular computing: basic issues and possible solutions. In: Proceedings of the 5th Joint Conference on Information Sciences. Citeseer (2000)Google Scholar
  13. 13.
    Filiberto, Y., et al.: Algoritmo para el aprendizaje de reglas de clasificación basado en la teoría de los conjuntos aproximados extendida. Dyna 78(169), 62–70 (2011)Google Scholar
  14. 14.
    Filiberto, Y., et al.: A method to build similarity relations into extended Rough Set Theory. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA). IEEE (2010)Google Scholar
  15. 15.
    Filiberto, Y., et al.: An analysis about the measure quality of similarity and its applications in machine learning. In: Fourth International Workshop on Knowledge Discovery, Knowledge Management and Decision Support. Atlantis Press (2013)Google Scholar
  16. 16.
    Filiberto Cabrera, Y., Bello Pérez, R., Mota, Y.C., Jimenez, G.R.: Improving the MLP learning by using a method to calculate the initial weights of the network based on the quality of similarity measure. In: Batyrshin, I., Sidorov, G. (eds.) MICAI 2011. LNCS (LNAI), vol. 7095, pp. 351–362. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-25330-0_31CrossRefGoogle Scholar
  17. 17.
    Filiberto, Y., et al.: Using PSO and RST to predict the resistant capacity of connections in composite structures. In: González, J.R., Pelta, D.A., Cruz, C., Terrazas, G., Krasnogor, N. (eds.) NICSO 2010. SCI, vol. 284, pp. 359–370. Springer, Heidelberg (2010).  https://doi.org/10.1007/978-3-642-12538-6_30CrossRefGoogle Scholar
  18. 18.
    Bello-García, M., García-Lorenzo, M.M., Bello, R.: A method for building prototypes in the nearest prototype approach based on similarity relations for problems of function approximation. In: Batyrshin, I., González Mendoza, M. (eds.) MICAI 2012. LNCS (LNAI), vol. 7629, pp. 39–50. Springer, Heidelberg (2013).  https://doi.org/10.1007/978-3-642-37807-2_4CrossRefGoogle Scholar
  19. 19.
    Fernandez, Y., et al.: Learning similarity measures from data with fuzzy sets and particle swarms. In: 2014 11th International Conference on Electrical Engineering, Computing Science and Automatic Control (CCE). IEEE (2014)Google Scholar
  20. 20.
    Wang, W.-J.: New similarity measures on fuzzy sets and on elements. Fuzzy Sets Syst. 85(3), 305–309 (1997)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Blake, C.L., Merz, C.J.: UCI repository of machine learning databases (1998) http://www.ics.uci.edu/~mlearn/MLRepository.html. Accessed 18 Mar 2018
  22. 22.
    Triguero, I., et al.: KEEL 3.0: an open source software for multi-stage analysis in data mining. Int. J. Comput. Intell. Syst. 10(1), 1238–1249 (2017)CrossRefGoogle Scholar
  23. 23.
    Bian, H., Mazlack, L.: Fuzzy-rough nearest-neighbor classification approach. In: In: NAFIPS 2003, 22nd International Conference of the North American Fuzzy Information Processing Society. IEEE (2003)Google Scholar
  24. 24.
    Sarkar, M.: Fuzzy-rough nearest neighbor algorithms in classification. Fuzzy Sets Syst. 158(19), 2134–2152 (2007)MathSciNetCrossRefGoogle Scholar
  25. 25.
    Jensen, R., Cornelis, C.: Fuzzy-rough nearest neighbour classification. In: Peters, J.F., Skowron, A., Chan, C.C., Grzymala-Busse, J.W., Ziarko, W.P. (eds.) Transactions on Rough Sets XIII. LNCS, vol. 6499, pp. 56–72. Springer, Heidelberg (2011).  https://doi.org/10.1007/978-3-642-18302-7_4CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2018

Authors and Affiliations

  • Yanela Rodríguez Alvarez
    • 1
  • Rafael Bello Pérez
    • 2
  • Yailé Caballero Mota
    • 1
  • Yaima Filiberto Cabrera
    • 1
  • Yumilka Fernández Hernández
    • 1
  • Mabel Frias Dominguez
    • 1
  1. 1.Departamento de ComputaciónUniversidad de CamagüeyCamagüeyCuba
  2. 2.Departamento de Ciencias de la ComputaciónUniversidad Central “Marta Abreu” de las VillasSanta ClaraCuba

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