Interpreting out-of-control signals using instance-based bayesian classifier in multivariate statistical process control

Abstract

In this article, an instance-based naive Bayes (INB) method is proposed to interpret out-of-control signals. By training one for one classifier, this method considers the similar features between test instance and training instances. For three benchmark examples with small number of variables, the experimental results show that INB outperforms all techniques in overall average performance; in cases of more than two variables, INB performs better in most scenarios. For two examples with large number of variables, the experimental results show that INB can be applied to practical problems. This research indicates that INB is very encouraging for interpreting the out-of-control signals in multivariate statistical process control.

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