An enhanced Monte Carlo outlier detection method |
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| Authors: | Liangxiao Zhang Peiwu Li Jin Mao Fei Ma Xiaoxia Ding Qi Zhang |
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| Affiliation: | 1. Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, China;2. Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, China;3. Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, China;4. Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, China;5. Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, China |
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| Abstract: | Outlier detection is crucial in building a highly predictive model. In this study, we proposed an enhanced Monte Carlo outlier detection method by establishing cross‐prediction models based on determinate normal samples and analyzing the distribution of prediction errors individually for dubious samples. One simulated and three real datasets were used to illustrate and validate the performance of our method, and the results indicated that this method outperformed Monte Carlo outlier detection in outlier diagnosis. After these outliers were removed, the value of validation by Kovats retention indices and the root mean square error of prediction decreased from 3.195 to 1.655, and the average cross‐validation prediction error decreased from 2.0341 to 1.2780. This method helps establish a good model by eliminating outliers. © 2015 Wiley Periodicals, Inc. |
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| Keywords: | outlier detection enhanced Monte Carlo outlier detection validation |
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