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Tabu search model selection for SVM.

Gilles Lebrun1, Christophe Charrier, Olivier Lezoray

  • 1Université de Caen Basse-Normandie, Marechal Juin, Caen, F-14050, France. gilles.lebrun@unicaen.fr

International Journal of Neural Systems
|March 18, 2008
PubMed
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This study introduces a tabu search method for optimizing support vector machines (SVMs). The approach enhances SVM classifier speed and efficiency by balancing recognition rates and model complexity.

Area of Science:

  • Machine Learning
  • Artificial Intelligence
  • Computer Science

Background:

  • Support Vector Machines (SVMs) are powerful classification algorithms.
  • Model complexity and generalization are key challenges in SVM development.
  • Efficient SVM classifiers are crucial for real-time applications.

Purpose of the Study:

  • To develop a model selection method for building efficient and generalized Support Vector Machines (SVMs).
  • To optimize SVM classifiers for speed and performance.
  • To create a quality criterion balancing recognition rate and decision function complexity.

Main Methods:

  • A tabu search algorithm is employed for model selection.
  • Vector quantization is used for selecting the simplification level.

Related Experiment Videos

  • Feature subset selection and SVM hyperparameter optimization are integrated.
  • A novel quality criterion combines recognition rate and model complexity.

    • Main Results:

    • The tabu search method effectively identifies sub-optimal SVM models within practical time constraints.
    • Reduced complexity and improved generalization of SVM classifiers were achieved.
    • The proposed method demonstrates a balance between classification accuracy and model efficiency.

    Conclusions:

    • The tabu search-based model selection is a viable approach for developing fast and efficient SVM classifiers.
    • Optimizing SVMs by considering both performance and complexity leads to better practical models.
    • This method offers a robust strategy for enhancing SVM performance in various applications.