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http://dx.doi.org/10.7232/JKIIE.2013.39.2.082

A Novelty Detection Algorithm for Multiple Normal Classes : Application to TFT-LCD Processes  

Joo, Tae Woo (School of Industrial Management Engineering, Korea University)
Kim, Seoung Bum (School of Industrial Management Engineering, Korea University)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.39, no.2, 2013 , pp. 82-89 More about this Journal
Abstract
Novelty detection (ND) is an effective technique that can be used to determine whether a future observation is normal or not. In the present study we propose a novelty detection algorithm that can handle a situation where the distributions of target (normal) observations are inhomogeneous. A simulation study and a real case with the TFT-LCD process demonstrated the effectiveness and usefulness of the proposed algorithm.
Keywords
Novelty Detection; Multiple Normal Classes; Mahalanobis Distance; Bootstrap Method; Data Mining; TFT-LCD Process;
Citations & Related Records
Times Cited By KSCI : 2  (Citation Analysis)
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