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A Comparative Study of Feature Selection Methods for Stress Hotspot Classification in Materials

Technical Article
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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.

Keywords

Stress hotspots Machine learning Random forests Crystal plasticity Titanium alloys Feature selection 
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Notes

Acknowledgements

This work was performed at Carnegie Mellon University. The authors are grateful to the authors of skfeature and sklearn python libraries who made their source code available through the Internet. We would also like to thank the reviewers for their thorough work. Ricardo Lebensohn of the Los Alamos National Laboratory is acknowledged for the use of the MASSIF code.

Funding Information

This work has been supported by the United States National Science Foundation award number DMR-1307138 and DMR-1507830.

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Copyright information

© The Minerals, Metals & Materials Society 2018

Authors and Affiliations

  1. 1.Department of Materials Science and EngineeringCarnegie Mellon UniversityPittsburghUSA

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