Communications for Statistical Applications and Methods

  • 제17권4호
  • /
  • Pages.561-574
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  • 2010
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  • 2287-7843(pISSN)
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  • 2383-4757(eISSN)
한국통계학회 (The Korean Statistical Society)
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앙상블기법을 이용한 다양한 데이터마이닝 성능향상 연구

A Study for Improving the Performance of Data Mining Using Ensemble Techniques

  • 투고 : 20100300
  • 심사 : 20100600
  • 발행 : 2010.07.31
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초록

본 논문은 8가지 방법의 데이터 마이닝 알고리즘(CART, QUEST, CRUISE, 로지스틱 회귀분석, 선형판별분석, 이차판별분석, 신경망분석, 서포트 벡터 머신) 기법과 단일 알고리즘에 2가지 앙상블기법(배깅, 부스팅)을 적용한 16가지 방법을 바탕으로 총 24가지의 방법을 비교하였다. 알고리즘의 성능 비교를 위하여 13개의 이항반응변수로 구성된 데이터를 사용하였다. 비교 기준은 민감도, 특이도 및 오분류율을 사용하여 데이터 마이닝 기법의 성능향상에 대해 평가하였다.

We studied the performance of 8 data mining algorithms including decision trees, logistic regression, LDA, QDA, Neral network, and SVM and their combinations of 2 ensemble techniques, bagging and boosting. In this study, we utilized 13 data sets with binary responses. Sensitivity, Specificity and missclassificate error were used as criteria for comparison.

키워드

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