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Support Vector Machine Algorithm for Imbalanced Data Learning

  • Journal of The Korea Society of Computer and Information
  • Abbr : JKSCI
  • 2010, 15(7), pp.11-17
  • Publisher : The Korean Society Of Computer And Information
  • Research Area : Engineering > Computer Science

김광성 1 Doosung Hwang 2

1현대정보기술
2단국대학교

Accredited

ABSTRACT

This paper proposes an improved SMO solving a quadratic optmization problem for class imbalanced learning. The SMO algorithm is aproporiate for solving the optimization problem of a support vector machine that assigns the different regularization values to the two classes, and the prosoposed SMO learning algorithm iterates the learning steps to find the current optimal solutions of only two Lagrange variables selected per class. The proposed algorithm is tested with the UCI benchmarking problems and compared to the experimental results of the SMO algorithm with the g-mean measure that considers class imbalanced distribution for gerneralization performance. In comparison to the SMO algorithm, the proposed algorithm is effective to improve the prediction rate of the minority class data and could shorthen the training time.

Citation status

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