다중공선성과 불균형분포를 가지는 공정데이터의 분류 성능 향상에 관한 연구A Study on Improving Classification Performance for Manufacturing Process Data with multicollinearity and Imbalanced Distribution
- Other Titles
- A Study on Improving Classification Performance for Manufacturing Process Data with multicollinearity and Imbalanced Distribution
- Authors
- 이채진; 박정술; 김준석; 백준걸
- Issue Date
- 2015
- Publisher
- 대한산업공학회
- Keywords
- Multicollinearity; Imbalanced Data; Multiple Hypothesis Testing; Weighted Decision Tree; Plasma Display Panel
- Citation
- 대한산업공학회지, v.41, no.1, pp.25 - 33
- Indexed
- KCI
- Journal Title
- 대한산업공학회지
- Volume
- 41
- Number
- 1
- Start Page
- 25
- End Page
- 33
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/95006
- DOI
- 10.7232/JKIIE.2015.41.1.025
- ISSN
- 1225-0988
- 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.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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