Relational variable for more accurate prediction of models

文献类型： 外文期刊

第一作者： Yuan, Zhe

作者： Yuan, Zhe;Zhang, Liangxiao;Yang, Ruinan;Mao, Jin;Zhang, Qi;Li, Peiwu;Yuan, Zhe;Yang, Ruinan;Zhang, Liangxiao;Li, Peiwu;Zhang, Qi;Li, Peiwu;Zhang, Liangxiao;Mao, Jin;Li, Peiwu;Zhang, Liangxiao

作者机构：

关键词： Relational variable; Model; Variable selection; Metabolomic; High accuracy

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

ISSN： 0169-7439

年卷期： 2018 年 180 卷

页码：

收录情况： SCI

摘要： In natural science, models could grant new insights into phenomena or scientific problems which are hard to be observed or otherwise explained to overcome the limitations of human beings. Routinely, scientists strive to develop new methods for data acquisition, preprocessing, variable selection, modeling and valuation with the help of statistics and machine learning theories. Theoretically, the aim of these methods is global or local optimization in the space of variables and linear/nonlinear combinations for classification or regression. However, the relationships between responses and features are often complex and therefore sometimes far from linear or fixed nonlinear model. In this study, we proposed the relational variable (e.g. ratio between two variables) for more accurate prediction performance of models and illustrated its application on three classic data. We found that the selected relational variables could significantly improve the accuracy of prediction. The software was complemented on the MATLAB R2015a platform in Windows Server 2012 R2 standard. The Matlab codes used in this study are publicly available at http://www.libpls.net.

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