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Relational variable for more accurate prediction of models

2018; Elsevier BV; Volume: 180; Linguagem: Inglês

10.1016/j.chemolab.2018.07.010

1873-3239

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

Cell Image Analysis Techniques

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.