Strongly Agree or Strongly Disagree?: Rating Features in Support Vector Machines

Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales

Research output: Working paperResearchpeer-review

Abstract

In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.
In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.
LanguageEnglish
Place of Publicationwww
PublisherMathematical Optimization Society
Number of pages22
StatePublished - 2013
Externally publishedYes
SeriesOptimization Online
Number4082
Volume10

Keywords

    Cite this

    Carrizosa, E., Nogales-Gómez, A., & Morales, D. R. (2013). Strongly Agree or Strongly Disagree? Rating Features in Support Vector Machines. www: Mathematical Optimization Society. Optimization Online, No. 4082, Vol.. 10
    Carrizosa, Emilio ; Nogales-Gómez, Amaya ; Morales, Dolores Romero. / Strongly Agree or Strongly Disagree? Rating Features in Support Vector Machines. www : Mathematical Optimization Society, 2013. (Optimization Online; No. 4082, ???volume??? 10).
    @techreport{f83eee384d924bebb1a828854b77cf2e,
    title = "Strongly Agree or Strongly Disagree?: Rating Features in Support Vector Machines",
    abstract = "In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.",
    keywords = "Support Vector Machines, Mixed Integer Linear Programming, Likert scale, Interpretability, Feature rating level",
    author = "Emilio Carrizosa and Amaya Nogales-G{\'o}mez and Morales, {Dolores Romero}",
    year = "2013",
    language = "English",
    publisher = "Mathematical Optimization Society",
    address = "United States",
    type = "WorkingPaper",
    institution = "Mathematical Optimization Society",

    }

    Carrizosa, E, Nogales-Gómez, A & Morales, DR 2013 'Strongly Agree or Strongly Disagree? Rating Features in Support Vector Machines' Mathematical Optimization Society, www.

    Strongly Agree or Strongly Disagree? Rating Features in Support Vector Machines. / Carrizosa, Emilio; Nogales-Gómez, Amaya; Morales, Dolores Romero.

    www : Mathematical Optimization Society, 2013.

    Research output: Working paperResearchpeer-review

    TY - UNPB

    T1 - Strongly Agree or Strongly Disagree?

    T2 - Rating Features in Support Vector Machines

    AU - Carrizosa,Emilio

    AU - Nogales-Gómez,Amaya

    AU - Morales,Dolores Romero

    PY - 2013

    Y1 - 2013

    N2 - In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.

    AB - In linear classifiers, such as the Support Vector Machine (SVM), a score is associated with each feature and objects are assigned to classes based on the linear combination of the scores and the values of the features. Inspired by discrete psychometric scales, which measure the extent to which a factor is in agreement with a statement, we propose the Discrete Level Support Vector Machine (DILSVM) where the feature scores can only take on a discrete number of values, defined by the so-called feature rating levels. The DILSVM classifier benefits from interpretability as it can be seen as a collection of Likert scales, one for each feature, where we rate the level of agreement with the positive class. To build the DILSVM classifier, we propose a Mixed Integer Linear Programming approach, as well as a collection of strategies to reduce the building times. Our computational experience shows that the 3-point and the 5-point DILSVM classifiers have comparable accuracy to the SVM with a substantial gain in interpretability and sparsity, thanks to the appropriate choice of the feature rating levels.

    KW - Support Vector Machines

    KW - Mixed Integer Linear Programming

    KW - Likert scale

    KW - Interpretability

    KW - Feature rating level

    M3 - Working paper

    BT - Strongly Agree or Strongly Disagree?

    PB - Mathematical Optimization Society

    CY - www

    ER -

    Carrizosa E, Nogales-Gómez A, Morales DR. Strongly Agree or Strongly Disagree? Rating Features in Support Vector Machines. www: Mathematical Optimization Society. 2013.