A New PLS and Bayesian Classification Based On-Line Outlier Detection Method

Tie Bin Wu; Yun Cheng; Zhi Kun Hu; Wen Ping Xie; Yun Lian Liu

doi:10.4028/www.scientific.net/AMM.397-400.1362

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 397-400A New PLS and Bayesian Classification Based...

A New PLS and Bayesian Classification Based On-Line Outlier Detection Method

Abstract:

Considering that multivariate data is difficult to detect, this paper propose an PLS and Bayesian theory based on-line outlier detection method. Firstly, it figures out the Q-statistics by PLS(partial least squares analysis), then classify Q statistics with Bayesian classification method and decide whether or not the sample data is normal. We employ UCI database to verify the method, the simulation results show that, compared to traditional PCA based method, it has lower ratio of error judgement, and is more effective in detecting outliers and identifying the change of process states.

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Periodical:

Applied Mechanics and Materials (Volumes 397-400)

Pages:

1362-1365

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.397-400.1362

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Online since:

September 2013

Authors:

Tie Bin Wu, Yun Cheng, Zhi Kun Hu, Wen Ping Xie, Yun Lian Liu

Keywords:

Bayesian Classification Method, Outlier Detection, Principal Component Analysis (PCA), Sliding Window Technique

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References

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