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IOWA Rough-Fuzzy Support Vector Data Description

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Information and Communication Technologies (TICEC 2022)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 1648))

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Abstract

Rough-Fuzzy Support Vector Data Description is a novel soft computing derivative of the classical Support Vector Data Description algorithm used in many real-world applications successfully. However, its current version treats all data points equally when constructing the classifier. If the data set contains outliers, they will substantially affect the decision boundary. To overcome this issue, we present a novel approach based on the induced ordered weighted average operator and linguistic quantifier functions to weigh data points depending on their closeness to the lower approximation of the target class. In this way, we determine the weights for the data points without using any external procedure. Our computational experiments emphasize the strength of the proposed approach underlining its potential for outlier detection.

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Notes

  1. 1.

    This term is not to be confused with the well-known concept of membership as defined in fuzzy logic.

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Acknowledgements

Both authors acknowledge financial support from FONDECYT Chile (1181036 and 1221562). The second author received financial support from ANID PIA/BASAL AFB180003.

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Authors and Affiliations

  1. Facultad de Innovación y Tecnología, Universidad Del Pacífico, Km. 7.5 Vía a la Costa, Guayaquil, Ecuador

    Ramiro Saltos

  2. Escuela de Negocios y Economía, Facultad de Ciencias Económicas y Administrativas, Pontificia Universidad Católica de Valparaíso, Amunátegui 1838, Viña del Mar, Chile

    Ramiro Saltos

  3. Department of Industrial Engineering, FCFM, Universidad de Chile, Av. Beaucheff 851, Santiago de Chile, Chile

    Richard Weber

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  1. Ramiro Saltos
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Correspondence to Ramiro Saltos .

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Editors and Affiliations

  1. Universidad Laica Eloy Alfaro de Manabí, Manta, Ecuador

    Jorge Herrera-Tapia

  2. Universidad Técnica Particular de Loja, Loja, Ecuador

    Germania Rodriguez-Morales

  3. Universidad de las Fuerzas Armadas ESPE, Quito, Ecuador

    Efraín R. Fonseca C.

  4. CEDIA, Cuenca, Ecuador

    Santiago Berrezueta-Guzman

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Saltos, R., Weber, R. (2022). IOWA Rough-Fuzzy Support Vector Data Description. In: Herrera-Tapia, J., Rodriguez-Morales, G., Fonseca C., E.R., Berrezueta-Guzman, S. (eds) Information and Communication Technologies. TICEC 2022. Communications in Computer and Information Science, vol 1648. Springer, Cham. https://doi.org/10.1007/978-3-031-18272-3_18

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  • DOI: https://doi.org/10.1007/978-3-031-18272-3_18

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