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Uncertainty Management with Fuzzy and Rough Sets pp 193–209Cite as

Similar Prototype Methods for Class Imbalanced Data Classification

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Part of the book series: Studies in Fuzziness and Soft Computing ((STUDFUZZ,volume 377))

Abstract

In this paper, new methods for solving imbalanced classification problems based on prototypes are proposed. Using similarity relations for the granulation of the universe, similarity classes are generated and a prototype is selected for each similarity class. Experimental results show that the performance of our methods is statistically superior to other imbalanced methods.

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Author information

Authors and Affiliations

  1. Departamento de Computación, Universidad de Camagüey, Circunvalación Norte Km 5 ½, Camagüey, Cuba

    Yanela Rodríguez Alvarez, Yailé Caballero Mota, Yaima Filiberto Cabrera, Isabel García Hilarión, Yumilka Fernández Hernández & Mabel Frias Dominguez

Authors
  1. Yanela Rodríguez Alvarez
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  2. Yailé Caballero Mota
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  3. Yaima Filiberto Cabrera
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  4. Isabel García Hilarión
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  5. Yumilka Fernández Hernández
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  6. Mabel Frias Dominguez
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Corresponding author

Correspondence to Yanela Rodríguez Alvarez .

Editor information

Editors and Affiliations

  1. Department of Computer Science, Universidad Central "Marta Abreu" de Las Villas, Santa Clara, Villa Clara, Cuba

    Rafael Bello

  2. School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON, Canada

    Rafael Falcon

  3. Department of Computer Science and Artificial Intelligence, Technical School of Informatics and Telecommunications Engineering, University of Granada, Granada, Spain

    José Luis Verdegay

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Alvarez, Y.R., Mota, Y.C., Cabrera, Y.F., Hilarión, I.G., Hernández, Y.F., Dominguez, M.F. (2019). Similar Prototype Methods for Class Imbalanced Data Classification. In: Bello, R., Falcon, R., Verdegay, J. (eds) Uncertainty Management with Fuzzy and Rough Sets. Studies in Fuzziness and Soft Computing, vol 377. Springer, Cham. https://doi.org/10.1007/978-3-030-10463-4_11

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