The Korean Journal of Applied Statistics (응용통계연구)

The Korean Statistical Society (한국통계학회)
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Gene Selection Based on Support Vector Machine using Bootstrap

붓스트랩 방법을 활용한 SVM 기반 유전자 선택 기법

  • Published : 2007.11.30
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Abstract

The recursive feature elimination for support vector machine is known to be useful in selecting relevant genes. Since the criterion for choosing relevant genes is the absolute value of a coefficient, the recursive feature elimination may suffer from a scaling problem. We propose a modified version of the recursive feature elimination algorithm using bootstrap. In our method, the criterion for determining relevant genes is the absolute value of a coefficient divided by its standard error, which accounts for statistical variability of the coefficient. Through numerical examples, we illustrate that our method is effective in gene selection.

본 연구에서는 유전자 선택 방법으로 최근 이용되는 SVM-RFE 알고리즘은 단순히 가중치의 절대값을 유전자 선택 기준으로 사용하여 유전자 값의 변동성을 고려하지 못하므로 가중치의 절대값을 그것의 표준오차로 나눈 보완된 통계량, B-RFE 알고리즘을 새로운 기준으로 제안하였다. 두 방법을 모의실험을 통해서 비교한 결과 본 연구에서 제안한 B-RFE 알고리즘이 더 의미 있는 순위를 도출하였다.

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References

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