A comparative study of feature selection methods for stress hotspot classification in materials

Autor:	Mangal, Ankita, Holm, Elizabeth A.
Rok vydání:	2018
Předmět:	Statistics - Machine Learning Condensed Matter - Materials Science Computer Science - Learning
Druh dokumentu:	Working Paper
DOI:	10.1007/s40192-018-0109-8
Popis:	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. Comment: under review in Integrating Materials and Manufacturing Innovation
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1804.09604 Zobrazit plný text záznamu View this record from Arxiv