Comparison of Novelty Score- Based Multivariate Control Charts
- Authors
- Tuerhong, Gulanbaier; Kim, Seoung Bum
- Issue Date
- 28-5월-2015
- Publisher
- TAYLOR & FRANCIS INC
- Keywords
- Data mining; Novelty score; Multivariate control charts; Quality control
- Citation
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION, v.44, no.5, pp.1126 - 1143
- Indexed
- SCIE
SCOPUS
- Journal Title
- COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION
- Volume
- 44
- Number
- 5
- Start Page
- 1126
- End Page
- 1143
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/93515
- DOI
- 10.1080/03610918.2013.809098
- ISSN
- 0361-0918
- Abstract
- Control charts are widely used in various industries to improve product quality. One recent trend in developing control charts is based on novelty score algorithms that can effectively describe reality and reflect the unique characteristics of the data being monitored. In this study, we compared eight novelty score algorithmsthe T-2, Local T-2, D-max, D-mean, K-2, the k-nearest neighbor data description, the local density outlier factor, and the hybrid novelty score (HNS)in terms of their average run length performance. A rigorous simulation was conducted to compare the novelty score-based multivariate control charts under both normal and non-normal scenarios. The simulation showed that in both normal and lognormal scenarios, D-max-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K-2-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T-2-based control charts outperformed the others.
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Collections - College of Engineering > School of Industrial and Management Engineering > 1. Journal Articles
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