An enhanced Monte Carlo outlier detection method

文献类型: 外文期刊

第一作者: Zhang, Liangxiao

作者: Zhang, Liangxiao;Li, Peiwu;Mao, Jin;Ma, Fei;Ding, Xiaoxia;Zhang, Qi;Zhang, Liangxiao;Mao, Jin;Zhang, Liangxiao;Li, Peiwu;Mao, Jin;Ding, Xiaoxia;Zhang, Liangxiao;Li, Peiwu;Ma, Fei;Li, Peiwu;Zhang, Qi

作者机构:

关键词: outlier detection;enhanced Monte Carlo outlier detection;validation

期刊名称:JOURNAL OF COMPUTATIONAL CHEMISTRY ( 影响因子:3.376; 五年影响因子:3.568 )

ISSN:

年卷期:

页码:

收录情况: SCI

摘要: 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. (c) 2015 Wiley Periodicals, Inc.

分类号: O6

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