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

Emilio Carrizosa, Amaya Nogales-Gómez, Dolores Romero Morales

Publikation: Working paperForskningpeer review

Resumé

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.
SprogEngelsk
Udgivelses stedwww
UdgiverMathematical Optimization Society
Antal sider22
StatusUdgivet - 2013
Udgivet eksterntJa
NavnOptimization Online
Nummer4082
Vol/bind10

Emneord

  • Support Vector Machines
  • Mixed Integer Linear Programming
  • Likert scale
  • Interpretability
  • Feature rating level

Citer dette

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, Nr. 4082, Bind. 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; Nr. 4082, ???volume??? 10).
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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.

Publikation: Working paperForskningpeer review

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T1 - Strongly Agree or Strongly Disagree?

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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.

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