Improvement on enhanced Monte-Carlo outlier detection method

文献类型： 外文期刊

第一作者： Zhang, Liangxiao

作者： Zhang, Liangxiao;Wang, Du;Li, Peiwu;Zhang, Wen;Mao, Jin;Yu, Li;Ding, Xiaoxia;Zhang, Qi;Zhang, Wen;Li, Peiwu;Zhang, Qi;Zhang, Liangxiao;Li, Peiwu;Mao, Jin;Ding, Xiaoxia;Zhang, Liangxiao;Wang, Du;Li, Peiwu;Zhang, Wen;Yu, Li;Zhang, Liangxiao;Gao, Rongrong

作者机构：

关键词： Outlier detection;Monte-Carlo sampling;Partial least squares regression;Multivariate calibration

期刊名称：CHEMOMETRICS AND INTELLIGENT LABORATORY SYSTEMS （ 影响因子：3.491； 五年影响因子：3.839 ）

ISSN：

年卷期：

页码：

收录情况： SCI

摘要： Highly predictive multivariate calibration model depends on samples in training set. In this study, we introduced an outlier detection method and developed its improvement for shorter run time. Improved Monte-Carlo outlier detection (IMCOD) was proposed to establish cross-prediction models for determining normal samples, which were subsequently used to analyze the distribution of prediction errors for all of dubious samples together. Four real datasets were employed to illustrate and validate the performance of IMCOD. After sample selection for training set of NIR of soy flour samples, the Root Mean Square Error of Prediction (RMSEP) of PIS model decreased from 1.4811 to 0.7650. This method benefits the establishment of a good model for QSAR and NIR datasets. (C) 2015 Elsevier B.V. All rights reserved.

分类号： TB9

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