A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials
Source abstract
Abstract
Abstract The first step in constructing a machine learning model is defining the features of the dataset 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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Research context
OpenAlex topics: Machine Learning in Materials Science · Metallurgy and Material Forming · Microstructure and Mechanical Properties of Steels
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References (45)
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