TY - JOUR
T1 - Comparison of novelty score-based multivariate control charts
AU - Tuerhong, Gulanbaier
AU - Kim, Seoung Bum
N1 - Funding Information:
This research was supported by Brain Korea 21 PLUS and Basic Science Research Program through the National Research Foundation of Korea funded by the Ministry of Science, ICT and Future Planning (2013007724).
Publisher Copyright:
© 2015 Taylor & Francis Group, LLC.
PY - 2015/5/7
Y1 - 2015/5/7
N2 - 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 algorithms - the T2, Local T2, Dmax, Dmean, K2, 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, Dmax-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K2-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T2-based control charts outperformed the others.
AB - 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 algorithms - the T2, Local T2, Dmax, Dmean, K2, 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, Dmax-based control charts produced the most promising results. In skewed distribution with high kurtosis non-normal scenarios, HNS- and K2-based control charts performed best. In symmetric with kurtosis non-normal scenarios, local T2-based control charts outperformed the others.
KW - Data mining
KW - Multivariate control charts
KW - Novelty score
KW - Quality control.
UR - http://www.scopus.com/inward/record.url?scp=84908621006&partnerID=8YFLogxK
U2 - 10.1080/03610918.2013.809098
DO - 10.1080/03610918.2013.809098
M3 - Article
AN - SCOPUS:84908621006
SN - 0361-0918
VL - 44
SP - 1126
EP - 1143
JO - Communications in Statistics Part B: Simulation and Computation
JF - Communications in Statistics Part B: Simulation and Computation
IS - 5
ER -