고차원 데이터의 분류를 위한 서포트 벡터 머신을 이용한 피처 감소 기법

Feature reduction for classifying high dimensional data sets using support vector machine

  • 고석하 (광주과학기술원 정보통신공학과) ;
  • 이현주 (광주과학기술원 정보통신공학과)
  • Ko, Seok-Ha (Department of Information and Communications Gwangju Institute of Science and Technology) ;
  • Lee, Hyun-Ju (Department of Information and Communications Gwangju Institute of Science and Technology)
  • 발행 : 2008.06.18

초록

We suggest a feature reduction method to classify mouse function data sets, which integrate several biological data sets represented as high dimensional vectors. To increase classification accuracy and decrease computational overhead, it is important to reduce the dimension of features. To do this, we employed Hybrid Huberized Support Vector Machine with kernels used for a kernel logistic regression method. When compared to support vector machine, this a pproach shows the better accuracy with useful features for each mouse function.

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