Journal of the Korean Operations Research and Management Science Society (한국경영과학회지)

The Korean Operations Research and Management Science Society (한국경영과학회)
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Set Covering-based Feature Selection of Large-scale Omics Data

Set Covering 기반의 대용량 오믹스데이터 특징변수 추출기법

  • Ma, Zhengyu (School of Industrial Management Engineering, Korea University) ;
  • Yan, Kedong (School of Information Management Engineering, Korea University) ;
  • Kim, Kwangsoo (Bioinformatics Institute, Seoul National University) ;
  • Ryoo, Hong Seo (School of Industrial Management Engineering, Korea University)
  • 마정우 (고려대학교 산업경영공학과) ;
  • 안기동 (고려대학교 정보경영공학과) ;
  • 김광수 (서울대학교 생물정보연구소) ;
  • 류홍서 (고려대학교 산업경영공학과)
  • Received : 2014.09.06
  • Accepted : 2014.11.04
  • Published : 2014.11.30
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Abstract

In this paper, we dealt with feature selection problem of large-scale and high-dimensional biological data such as omics data. For this problem, most of the previous approaches used simple score function to reduce the number of original variables and selected features from the small number of remained variables. In the case of methods that do not rely on filtering techniques, they do not consider the interactions between the variables, or generate approximate solutions to the simplified problem. Unlike them, by combining set covering and clustering techniques, we developed a new method that could deal with total number of variables and consider the combinatorial effects of variables for selecting good features. To demonstrate the efficacy and effectiveness of the method, we downloaded gene expression datasets from TCGA (The Cancer Genome Atlas) and compared our method with other algorithms including WEKA embeded feature selection algorithms. In the experimental results, we showed that our method could select high quality features for constructing more accurate classifiers than other feature selection algorithms.

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References

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