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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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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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