Journal of Digital Contents Society (디지털콘텐츠학회 논문지)

Digital Contents Society (한국디지털콘텐츠학회)
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A Hybrid Efficient Feature Selection Model for High Dimensional Data Set based on KNHNAES (2013~2015)

KNHNAES (2013~2015) 에 기반한 대형 특징 공간 데이터집 혼합형 효율적인 특징 선택 모델

  • Kwon, Tae il (BigSun Systems Co. LTd.) ;
  • Li, Dingkun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Park, Hyun Woo (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Ryu, Kwang Sun (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Kim, Eui Tak (Database/Bioinformatics Lab, School of Electrical & Computer Engineering, Chungbuk National University) ;
  • Piao, Minghao (Agency of Smart Factory, Chungbuk National University)
  • 권태일 (빅썬시스템즈(주)) ;
  • 이정곤 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실) ;
  • 박현우 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실) ;
  • 류광선 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실) ;
  • 김의탁 (충북대학교 전기전자정보컴퓨터학부 데이터베이스/바이오인포매틱스연구실) ;
  • 박명호 (충북대학교 스마트 팩토리 사업단)
  • Received : 2018.04.05
  • Accepted : 2018.04.25
  • Published : 2018.04.30
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Abstract

With a large feature space data, feature selection has become an extremely important procedure in the Data Mining process. But the traditional feature selection methods with single process may no longer fit for this procedure. In this paper, we proposed a hybrid efficient feature selection model for high dimensional data. We have applied our model on KNHNAES data set, the result shows that our model outperforms many existing methods in terms of accuracy over than at least 5%.

고차원 데이터에서는 데이터마이닝 기법 중에서 특징 선택은 매우 중요한 과정이 되었다. 그러나 전통적인 단일 특징 선택방법은 더 이상 효율적인 특징선택 기법으로 적합하지 않을 수 있다. 본 논문에서 우리는 고차원 데이터에 대한 효율적인 특징선택을 위하여 혼합형 특징선택 기법을 제안하였다. 본 논문에서는 KNHANES 데이터에 제안한 혼합형 특징선택기법을 적용하여 분류한 결과 기존의 분류기법을 적용한 모델보다 5% 이상의 정확도가 향상되었다.

Keywords

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