An enhanced Monte Carlo outlier detection method. Issue 25 (31st July 2015)

• Record Type: Journal Article
• Authors: Zhang, Liangxiao; Li, Peiwu; Mao, Jin; Ma, Fei; Ding, Xiaoxia; Zhang, Qi
• Is Part Of: Journal of computational chemistry. Volume 36:Issue 25(2015)
• Page Start: 1902
• Page End: 1906
• Publication Date: 2015-07-31
• Subjects: Chemistry -- Data processing -- Periodicals; 542.85
• DOI: 10.1002/jcc.24026
• Languages: English
• ISSNs: 0192-8651

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>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.</p> </abstract>