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- W2874612972 abstract "This paper focuses on improving data environment in the context of industrial process monitoring. Considering the negative influence of outliers in target set, this paper proposes a novel two-level ensemble model, in which the base learner is SVDD. With data processed by our method, performance of data-based techniques dedicating to process monitoring can be improved significantly. The Tennessee Eastman process is used to verify the significance of our method in terms of process monitoring. Experimental results approve the potential and necessity of our method." @default.
- W2874612972 created "2018-07-19" @default.
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- W2874612972 date "2018-06-01" @default.
- W2874612972 modified "2023-09-25" @default.
- W2874612972 title "A novel outlier detection method for improving industrial process monitoring" @default.
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- W2874612972 doi "https://doi.org/10.1109/ccdc.2018.8407303" @default.
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