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首页> 外文期刊>Journal of separation science. >Quantitative structure-property relationships of retention indices of some sulfur organic compounds using random forest technique as a variable selection and modeling method
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Quantitative structure-property relationships of retention indices of some sulfur organic compounds using random forest technique as a variable selection and modeling method

机译：利用随机森林技术作为变量选择和建模方法，对某些含硫有机物的保留指数进行定量构效关系

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摘要

In this work, a noble quantitative structure-property relationship technique is proposed on the basis of the random forest for prediction of the retention indices of some sulfur organic compounds. In order to calculate the retention indices of these compounds, the theoretical descriptors produced using their molecular structures are employed. The influence of the significant parameters affecting the capability of the developed random forest prediction power such as the number of randomly selected variables applied to split each node (m) and the number of trees (nt) is studied to obtain the best model. After optimizing the nt and m parameters, the random forest model conducted for m = 70 and nt = 460 was found to yield the best results. The artificial neural network and multiple linear regression modeling techniques are also used to predict the retention index values for these compounds for comparison with the results of random forest model. The descriptors selected by the stepwise regression and random forest model are used to build the artificial neural network models. The results achieved showed the superiority of the random forest model over the other models for prediction of the retention indices of the studied compounds.
机译：在这项工作中，基于随机森林提出了一种高贵的定量结构-性质关系技术，以预测某些硫有机化合物的保留指数。为了计算这些化合物的保留指数，采用了使用其分子结构产生的理论描述词。研究了影响已开发的随机森林预测能力的重要参数的影响，例如用于分割每个节点的随机选择变量的数量（m）和树木数量（nt），以获得最佳模型。优化nt和m参数后，发现针对m = 70和nt = 460进行的随机森林模型产生了最佳结果。人工神经网络和多元线性回归建模技术还用于预测这些化合物的保留指数值，以便与随机森林模型的结果进行比较。通过逐步回归和随机森林模型选择的描述符用于构建人工神经网络模型。获得的结果表明，随机森林模型优于其他模型可预测所研究化合物的保留指数。

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