title: An enhanced Monte Carlo outlier detection method.
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.
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062, China.|Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, 430062, China.|Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Liangxiao
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, 430062, China.|Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, 430062, China.|Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Peiwu
authors.initials: P
authors.lastname: Li
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Key Laboratory of Biology and Genetic Improvement of Oil Crops, Ministry of Agriculture, Wuhan, 430062, China.|Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Jin
authors.initials: J
authors.lastname: Mao
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Quality Inspection and Test Center for Oilseeds Products, Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Fei
authors.initials: F
authors.lastname: Ma
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Laboratory of Risk Assessment for Oilseeds Products (Wuhan), Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Xiaoxia
authors.initials: X
authors.lastname: Ding
authors.affiliation: Oil Crops Research Institute, Chinese Academy of Agricultural Sciences, Wuhan, 430062, China.|Key Laboratory of Detection for Mycotoxins, Ministry of Agriculture, Wuhan, 430062, China.
authors.forename: Qi
authors.initials: Q
authors.lastname: Zhang
mesh_terms: D057225:Data Mining / Q000379:methods*; D066264:Datasets as Topic; D015233:Models, Statistical; D009010:Monte Carlo Method
keywords: enhanced Monte Carlo outlier detection; outlier detection; validation