Extended Gaussian mixture regression for forward and inverse analysis
Extended Gaussian mixture regression for forward and inverse analysis
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DOI：
10.1016/j.chemolab.2021.104325
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发表时间：
2021-04-29
影响因子：
3.9
通讯作者：
Kaneko, Hiromasa
中科院分区：
文献类型：
--
作者：
Kaneko, Hiromasa
在分子、材料和工艺设计中，使用性能和活性的目标值对机器学习构建的回归模型进行逆分析非常重要。虽然许多方法实际上采用伪逆分析，但高斯混合回归（GMR）可以实现直接逆分析。本文介绍了扩展GMR（EGMR）的发展和使用，它提供了改进的预测能力比传统的GMR。EGMR包括GMR和贝叶斯GMR的实现，后者基于变分贝叶斯方法。每个模型的超参数都经过优化，并通过交叉验证确定特定数据的模型选择。建议EGMR的有效性进行了验证，使用数值模拟数据集，复合数据集，材料数据集，光谱数据集。这些数据集包含真实的数据。EGMR的预测能力在所有情况下都大于或等于GMR，预测误差可减少30%以上。此外，它被证实，EGMR可以进行逆分析，具有高再现性，即使在外推区域的目标变量。EGMR的Python代码可在https://github.com/hkaneko1985/dcekit上获得。
In molecular, material, and process designs, it is important to perform inverse analysis of the regression models constructed with machine learning using target values of the properties and activities. Although many approaches actually employ a pseudo-inverse analysis, Gaussian mixture regression (GMR) can achieve direct inverse analysis. This paper describes the development and use of extended GMR (EGMR), which offers improved predictive ability over conventional GMR. EGMR includes implementations of both GMR and Bayesian GMR, which is based on a variational Bayesian method. The hyperparameters for each model are optimized, and the choice of model for the specific data is determined, through cross-validation. The effectiveness of the proposed EGMR is verified using numerically simulated datasets, compound datasets, a material dataset, and spectral datasets. These datasets contain real data. The predictive ability of EGMR is found to be greater than or equal to that of GMR in all cases, and the prediction errors can be reduced by more than 30%. Furthermore, it is confirmed that EGMR can perform inverse analysis with high reproducibility, even in the extrapolation region of an objective variable. The Python code for EGMR is available at https://github.com/hkaneko1985/dcekit.