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An Algebraic Approach to Clustering and Classification with Support Vector Machines

Author

Listed:
  • Güvenç Arslan

    (Department of Statistics, Kırıkkale University, Kırıkkale 71450, Turkey)

  • Uğur Madran

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

  • Duygu Soyoğlu

    (College of Engineering and Technology, American University of the Middle East, Egaila 54200, Kuwait)

Abstract

In this note, we propose a novel classification approach by introducing a new clustering method, which is used as an intermediate step to discover the structure of a data set. The proposed clustering algorithm uses similarities and the concept of a clique to obtain clusters, which can be used with different strategies for classification. This approach also reduces the size of the training data set. In this study, we apply support vector machines (SVMs) after obtaining clusters with the proposed clustering algorithm. The proposed clustering algorithm is applied with different strategies for applying SVMs. The results for several real data sets show that the performance is comparable with the standard SVM while reducing the size of the training data set and also the number of support vectors.

Suggested Citation

  • Güvenç Arslan & Uğur Madran & Duygu Soyoğlu, 2022. "An Algebraic Approach to Clustering and Classification with Support Vector Machines," Mathematics, MDPI, vol. 10(1), pages 1-19, January.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:1:p:128-:d:716077
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    References listed on IDEAS

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    1. Wenbiao Yang & Kewen Xia & Tiejun Li & Min Xie & Fei Song, 2021. "A Multi-Strategy Marine Predator Algorithm and Its Application in Joint Regularization Semi-Supervised ELM," Mathematics, MDPI, vol. 9(3), pages 1-34, February.
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    Cited by:

    1. Junya Tang & Kuo-Yi Lin & Li Li, 2022. "Using Domain Adaptation for Incremental SVM Classification of Drift Data," Mathematics, MDPI, vol. 10(19), pages 1-17, September.

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