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Toward Improving the Fuzzy KNN Algorithm Based on Takagi–Sugeno Fuzzy Inference System

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Fuzzy Information Processing 2020 (NAFIPS 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1337))

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Abstract

In this paper we present a new approach in order to improve the performance of the Fuzzy K-Nearest Neighbor algorithm (Fuzzy KNN algorithm). We propose to use a Takagi–Sugeno Fuzzy Inference System with the Fuzzy KNN algorithm to improve classification accuracy. Also, we have used different measures to calculate the distance between the neighbors and the vector to be classified, such as the: Euclidean, Hamming, cosine similarity and city block distances. These distances represent the inputs for the Takagi–Sugeno Fuzzy Inference System. Simulation results with a classification problem show the potential of the proposed approach.

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References

  1. D.W. Aha, D. Kibler, M.K. Albert, Instance-based learning algorithms. Mach. Learn. 6, 37–66 (1991)

    Google Scholar 

  2. J.M. Keller, M.R. Gray, J.A. Givens, A fuzzy k-nearest neighbor algorithm. IEEE Trans. Fuzzy Syst., Man, Cybern. 15, 580–585 (1985)

    Article  Google Scholar 

  3. A. Jowik, A learning scheme for fuzzy k-NN rule. Pattern Recognit. Lett. 1, 287–289 (1983)

    Google Scholar 

  4. M. Yang, C. Chen, On strong consistency of the fuzzy generalized nearest neighbor rule. Fuzzy Sets Syst. 60, 273–281 (1993)

    Article  MathSciNet  Google Scholar 

  5. F. Chung-Hoon, C. Hwang, An interval type-2 fuzzy k-nearest neighbor, in Proceedings of the 12th IEEE International Conference on Fuzzy Systems (IEEE FUZZY’03) (St. Louis, Missouri, USA), pp. 802–807. May 25–29

    Google Scholar 

  6. T. Denoeux, A k-nearest neighbor classification rule based on Dempster-Shafer theory. IEEE Trans. Syst. Man Cybern. 25, 804–813 (1995)

    Article  Google Scholar 

  7. S. Destercke, An k-nearest neighbors method based on imprecise probabilities. Soft. Comput. 16, 833–844 (2012)

    Article  Google Scholar 

  8. S. Hadjittodorov, An intuituionistic fuzzy sets applicaion to the k-NN method. Notes Intuitionistic Fuzzy Sets 1, 66–69 (1995)

    MathSciNet  Google Scholar 

  9. H. Bian, L. Mazlack, Fuzzy-rough nearest neighbor classification approach, in Proceedings of the 22th International Conference of the North American Fuzzy Information Processings Society (NAFIPS’03) (Chicago, Illinois, USA, 2003), pp. 500–505. July 24–26

    Google Scholar 

  10. J.H. Zhai, N. Li, M.Y. Zhai, The condensed fuzzy k-nearest neighbor rule based on sample fuzzy entropy, in Proceedings of the 2011 International Conference on Machine Learning and Cybernetics (ICMLC’11) (Guilin, China), pp. 282–286. July 10–13

    Google Scholar 

  11. S. Garcia, J. Derrac, J.R. Cano, F. Herrera, Prototype selection for nearest neighbor classification: taxonomy and empirical study. IEEE Trans. Pattern Anal. Mach. Intell. 34, 417–435 (2012)

    Article  Google Scholar 

  12. J. Derrac, S. Garcia, F. Herrera, Fuzzy nearest neighbor algorithms: taxonomy, experimental analysis and prospects. Inf. Sci. 260, 98–119 (2014)

    Article  Google Scholar 

  13. O. Castillo, P. Melin, E. Ramirez, J. Soria, Hybrid intelligent system for cardiac arrythmia classification with fuzzy k-nearest neighbors and neural networks combined with a fuzzy system. Expert Syst. Appl. 39, 2947–2955 (2012)

    Article  Google Scholar 

  14. J.M. Mendel, Explaining the performance potential of rule-based fuzzy systems as a greater sculpting of the state space. IEEE Trans. Fuzzy Syst. 2199–2216 (2017)

    Google Scholar 

  15. P. Melin, O. Castillo, A review on the applications of type-2 fuzzy logic in classification and pattern recognition. Expert Syst. Appl. 40(13), 5413–5423 (2013)

    Article  Google Scholar 

  16. O. Castillo, L. Amador-Angulo, J.R. Castro, M. Garcia-Valdez, A comparative study of type-1 fuzzy logic systems, interval type-2 fuzzy logic systems and generalized type-2 fuzzy logic systems in control problems. Inf. Sci. 354, 257–274 (2016)

    Article  Google Scholar 

  17. M. Yang, C. Cheng, On the edited fuzzy k-nearest neighbor rule. IEEE Trans. Syst. Man Cybern. B Cybern. 28, 461–466 (1998)

    Article  Google Scholar 

  18. P. Melin, J. Amezcua, F. Valdez, O. Castillo, A new neural network model based on the LVQ algorithm for multi-class classification of arrhythmias. Inf. Sci 279, 483–497 (2014)

    Article  MathSciNet  Google Scholar 

  19. E. Ramirez, O. Castillo, J. Soria, Hybrid system for cardiac arrhythmia classification with fuzzy K-nearest neighbors and neural networks combined by a fuzzy inference system. Soft Comput. Recognit. Based Biom., Stud. Comput. Intell. 312, 37–53. ISBN 978-3-642-15110-1. (Springer, 2010)

    Google Scholar 

  20. P. Melin, E. Ramirez, G. Prado-Arechiga, A new variant of fuzzy K-nearest neighbor using interval type-2 fuzzy logic. FUZZ-IEEE, 1–7 (2018)

    Google Scholar 

  21. R. Jayasinghe, ECG Workbook (Churchill Livingstone, Elsevier, 2012)

    Google Scholar 

  22. E. Ramirez, P. Melin, G. Prado-Arechiga, Hybrid model based on neural networks, type-1 and type-2 fuzzy systems for 2-lead cardiac arrhythmia classification. Expert. Syst. Appl., Elsevier 126, 295–307 (2019)

    Article  Google Scholar 

  23. E. Ramirez, P. Melin, G. Prado-Arechiga, Hybrid Model Based on Neural Networks and Fuzzy Logic for 2-Lead Cardiac Arrhythmia Classification (Hybrid Intelligent Systems in Control, Pattern Recognition and Medicine, 2020), pp. 193–217

    MATH  Google Scholar 

  24. G. Moody, R. Mark, The impact of the MIT-BIH arrhythmia database. IEEE Eng. Med. Biol. 20(3), 45–50 (2001)

    Article  Google Scholar 

  25. A. Goldberger, L. Amaral, L. Glass, J. Hausdorff, P. Ivanov, R. Mark, J. Mietus, G. Moody, C. Peng, H. Stanley, PhysioBank, physiotoolkit, and physionet: components of a new research resource for complex physiologic signals. Circulation 101(23), e215–e220 (2003)

    Google Scholar 

  26. J. Amezcua, P. Melin, O. Castillo, A new classification method based on LVQ neural networks and fuzzy logic. NAFIPS/WConSC, 1–5 (2015)

    Google Scholar 

  27. J. Martindale, D. Brown, A visual guide to ECG interpretation, 2nd edn. (Wolters Kluwer, 2017). ISBN 978-1-4963-2153-4

    Google Scholar 

  28. Z. Takac, H. Bustince, J. Pintor, C. Marco-D, Width-based interval-value distances and fuzzy entropies. IEEE Access 7, 14044–14057 (2019)

    Article  Google Scholar 

  29. H. Salles, I. Couso, B. Bedregal, Z. Takac, M. Minarova, A. Asiain, E. Barrenechea, H. Bustince, Similarity measures, penalty functions and fuzzy entropy form new subsethood measures. Int. J. Intell. Syst. 34(6), 1281–1302 (2019)

    Article  Google Scholar 

  30. P. Melin, D. Sánchez, O. Castillo, Genetic optimization of modular neural networks with fuzzy response integration for human recognition. Inf. Sci. 197, 1–19 (2012)

    Article  Google Scholar 

  31. M.A. Sanchez, O. Castillo, J.R. Castro, P. Melin, Fuzzy granular gravitational clustering algorithm for multivariate data. Inf. Sci. 279, 498–511 (2014)

    Article  MathSciNet  Google Scholar 

  32. D. Sanchez, P. Melin, Optimization of modular granular neural networks using hierarchical genetic algorithms for human recognition using the ear biometric measure. Eng. Appl. Artif. Intell. 27, 41–56 (2014)

    Article  Google Scholar 

  33. S. Hadjitodorov, An intuitionistic fuzzy sets application to the k-NN method. Notes Intuitionistic Fuzzy Sets 1, 66–69 (1995)

    MathSciNet  Google Scholar 

  34. N. Gayar, F. Schwenker, G. Palm, A study of the robustness of kNN classifiers trained using soft labels, in Proceedings of the Second IAPR Workshop on Artificial Neural Networks in Pattern Recognition (ANNPR 2006) (Ulm, Germany), pp. 67–80. August 31–September 2

    Google Scholar 

  35. M. Arif, M.U. Akram, F.A. Afsar, F.A.A. Minhas, Pruned fuzzy k-nearest neighbor classifier for beat classification. J. Biomed. Sci. Eng. 3 (2010)

    Google Scholar 

  36. O. Castillo, Type-2 Fuzzy Logic in Intelligent Control Applications (Springer, 2012)

    Google Scholar 

  37. E. Ontiveros-Robles, P. Melin, O. Castillo, Comparative analysis of noise robustness of type 2 fuzzy logic controllers. Kybernetika 54(1), 175–201 (2018)

    MathSciNet  MATH  Google Scholar 

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Author information

Authors and Affiliations

  1. Tijuana Institute of Technology, Tijuana, BC, Mexico

    Eduardo Ramírez & Patricia Melin

  2. Tijuana Institute of Technology, 22010, Tijuana, BC, Mexico

    German Prado-Arechiga

Authors
  1. Eduardo Ramírez
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  2. Patricia Melin
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  3. German Prado-Arechiga
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Corresponding author

Correspondence to Patricia Melin .

Editor information

Editors and Affiliations

  1. Department of Mathematics, DigiPen Institute of Technology, Redmond, WA, USA

    Barnabás Bede

  2. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Martine Ceberio

  3. School of Engineering and Technology, University of Washington, Tacoma, WA, USA

    Martine De Cock

  4. Department of Computer Science, University of Texas at El Paso, El Paso, TX, USA

    Vladik Kreinovich

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Ramírez, E., Melin, P., Prado-Arechiga, G. (2022). Toward Improving the Fuzzy KNN Algorithm Based on Takagi–Sugeno Fuzzy Inference System. In: Bede, B., Ceberio, M., De Cock, M., Kreinovich, V. (eds) Fuzzy Information Processing 2020. NAFIPS 2020. Advances in Intelligent Systems and Computing, vol 1337. Springer, Cham. https://doi.org/10.1007/978-3-030-81561-5_20

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