材料应力热点分类的特征选择方法比较研究
A comparative study of feature selection methods for stress hotspot classification in materials
摘要：
构建机器学习模型的第一步是定义可用于优化学习的数据集的特征。在这项工作中，我们讨论的特征选择方法，可以用来建立更好的模型，以及实现模型的可解释性。我们将这些方法应用于应力热点分类问题的背景下，以确定什么样的微观结构特征可以导致应力在某些晶粒在单轴拉伸变形过程中建立。结果表明，一些功能选择技术是有偏见的，并展示了一个首选的技术，以获得物理解释的功能排名。
英文摘要：
The first step in constructing a machine learning model is defining the features of the data set that can be used for optimal learning. In this work we discuss feature selection methods, which can be used to build better models, as well as achieve model interpretability. We applied these methods in the context of stress hotspot classification problem, to determine what microstructural characteristics can cause stress to build up in certain grains during uniaxial tensile deformation. The results show how some feature selection techniques are biased and demonstrate a preferred technique to get feature rankings for physical interpretations.
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