Clustering Categories in Support Vector Machines

Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales

Research output: Working paperResearchpeer-review

Abstract

Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the clustered dataset. Four strategies for building the CLSVM classifier are presented based on solving: the original SVM formulation, a Quadratically Constrained Quadratic Programming formulation, and a Mixed Integer Quadratic Programming formulation as well as its continuous relaxation. The computational study illustrates the performance of the CLSVM classifier using two clusters. In the tested datasets our methodology achieves comparable accuracy to that of the SVM with original data but with a dramatic decrease in complexity.
Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the clustered dataset. Four strategies for building the CLSVM classifier are presented based on solving: the original SVM formulation, a Quadratically Constrained Quadratic Programming formulation, and a Mixed Integer Quadratic Programming formulation as well as its continuous relaxation. The computational study illustrates the performance of the CLSVM classifier using two clusters. In the tested datasets our methodology achieves comparable accuracy to that of the SVM with original data but with a dramatic decrease in complexity.
LanguageEnglish
Place of Publicationwww
PublisherMathematical Optimization Society
Number of pages20
StatePublished - 2014
Externally publishedYes
SeriesOptimization Online
Number4403
Volume06

Keywords

    Cite this

    Carrizosa, E., Nogales-Gómez, A., & Morales, D. R. (2014). Clustering Categories in Support Vector Machines. www: Mathematical Optimization Society. Optimization Online, No. 4403, Vol.. 06
    Carrizosa, Emilio ; Nogales-Gómez, Amaya ; Morales, Dolores Romero. / Clustering Categories in Support Vector Machines. www : Mathematical Optimization Society, 2014. (Optimization Online; No. 4403, ???volume??? 06).
    @techreport{e863502c734a40958328dfe1ad784b05,
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    abstract = "Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the clustered dataset. Four strategies for building the CLSVM classifier are presented based on solving: the original SVM formulation, a Quadratically Constrained Quadratic Programming formulation, and a Mixed Integer Quadratic Programming formulation as well as its continuous relaxation. The computational study illustrates the performance of the CLSVM classifier using two clusters. In the tested datasets our methodology achieves comparable accuracy to that of the SVM with original data but with a dramatic decrease in complexity.",
    keywords = "Support vector machines, Categorical features, Classifier complexity, Clustering, Quadratically constrained programming, 0-1 programming",
    author = "Emilio Carrizosa and Amaya Nogales-G{\'o}mez and Morales, {Dolores Romero}",
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    Carrizosa, E, Nogales-Gómez, A & Morales, DR 2014 'Clustering Categories in Support Vector Machines' Mathematical Optimization Society, www.

    Clustering Categories in Support Vector Machines. / Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero.

    www : Mathematical Optimization Society, 2014.

    Research output: Working paperResearchpeer-review

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    AU - Carrizosa,Emilio

    AU - Nogales-Gómez,Amaya

    AU - Morales,Dolores Romero

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    N2 - Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the clustered dataset. Four strategies for building the CLSVM classifier are presented based on solving: the original SVM formulation, a Quadratically Constrained Quadratic Programming formulation, and a Mixed Integer Quadratic Programming formulation as well as its continuous relaxation. The computational study illustrates the performance of the CLSVM classifier using two clusters. In the tested datasets our methodology achieves comparable accuracy to that of the SVM with original data but with a dramatic decrease in complexity.

    AB - Support Vector Machines (SVM) is the state-of-the-art in Supervised Classification. In this paper the Cluster Support Vector Machines (CLSVM) methodology is proposed with the aim to reduce the complexity of the SVM classifier in the presence of categorical features. The CLSVM methodology lets categories cluster around their peers and builds an SVM classifier using the clustered dataset. Four strategies for building the CLSVM classifier are presented based on solving: the original SVM formulation, a Quadratically Constrained Quadratic Programming formulation, and a Mixed Integer Quadratic Programming formulation as well as its continuous relaxation. The computational study illustrates the performance of the CLSVM classifier using two clusters. In the tested datasets our methodology achieves comparable accuracy to that of the SVM with original data but with a dramatic decrease in complexity.

    KW - Support vector machines

    KW - Categorical features

    KW - Classifier complexity

    KW - Clustering

    KW - Quadratically constrained programming

    KW - 0-1 programming

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    Carrizosa E, Nogales-Gómez A, Morales DR. Clustering Categories in Support Vector Machines. www: Mathematical Optimization Society. 2014.