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Mexican International Conference on Artificial Intelligence

MICAI 2017: Advances in Soft Computing pp 325–335Cite as

Towards a Classification of Binary Similarity Measures

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10632))

Abstract

Similarity measures for binary variables are used in many problems of machine learning, pattern recognition and classification. Currently, the dozens of similarity measures are introduced and the problem of comparative analysis of these measures appears. One of the methods used for such analysis is clustering of similarity measures based on correlation between data similarity values obtained by different measures. The paper proposes the method of comparative analysis of similarity measures based on the set theoretic representation of these measures and comparison of algebraic properties of these representations. The results show existing relationship between results of clustering and the classification of measures by their properties. Due to the results of clustering depend on the clustering method and on data used for measuring correlation between measures we conclude that the classification based on the proposed properties of similarity measures is more suitable for comparative analysis of similarity measures.

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Acknowledgements

The work is partially supported by the projects SIP 20171344, BEIFI of IPN and 283778 of CONACYT.

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Authors and Affiliations

  1. Centro de Investigación en Computación, Instituto Politécnico Nacional, Av. Juan de Dios Bátiz, Esq. Miguel Othón de Mendizábal S/N, Nueva Industrial Vallejo, 07738, Ciudad de México, Mexico

    Ivan Ramirez Mejia & Ildar Batyrshin

Authors
  1. Ivan Ramirez Mejia
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  2. Ildar Batyrshin
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Corresponding author

Correspondence to Ildar Batyrshin .

Editor information

Editors and Affiliations

  1. Universidad Autónoma del Estado de Hidalgo, Pachuca, Mexico

    Félix Castro

  2. INFOTEC Aguascalientes, Aguascalientes, Mexico

    Sabino Miranda-Jiménez

  3. Tecnológico de Monterrey, Atizapán de Zaragoza, Mexico

    Miguel González-Mendoza

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Mejia, I.R., Batyrshin, I. (2018). Towards a Classification of Binary Similarity Measures. In: Castro, F., Miranda-Jiménez, S., González-Mendoza, M. (eds) Advances in Soft Computing. MICAI 2017. Lecture Notes in Computer Science(), vol 10632. Springer, Cham. https://doi.org/10.1007/978-3-030-02837-4_27

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  • DOI: https://doi.org/10.1007/978-3-030-02837-4_27

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-02836-7

  • Online ISBN: 978-3-030-02837-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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