Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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A Study on Improving Classification Performance for Manufacturing Process Data with Multicollinearity and Imbalanced Distribution

다중공선성과 불균형분포를 가지는 공정데이터의 분류 성능 향상에 관한 연구

  • 이채진 (LG전자 HE사업본부) ;
  • 박정술 (고려대학교 산업경영공학과) ;
  • 김준석 (고려대학교 산업경영공학과) ;
  • 백준걸 (고려대학교 산업경영공학과)
  • Received : 2014.01.20
  • Accepted : 2014.11.03
  • Published : 2015.02.15
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Abstract

From the viewpoint of applications to manufacturing, data mining is a useful method to find the meaningful knowledge or information about states of processes. But the data from manufacturing processes usually have two characteristics which are multicollinearity and imbalance distribution of data. Two characteristics are main causes which make bias to classification rules and select wrong variables as important variables. In the paper, we propose a new data mining procedure to solve the problem. First, to determine candidate variables, we propose the multiple hypothesis test. Second, to make unbiased classification rules, we propose the decision tree learning method with different weights for each category of quality variable. The experimental result with a real PDP (Plasma display panel) manufacturing data shows that the proposed procedure can make better information than other data mining procedures.

Keywords

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