IEEJ Transactions on Fundamentals and Materials
Online ISSN : 1347-5533
Print ISSN : 0385-4205
ISSN-L : 0385-4205
Special Issue Paper
Evaluation of Machine Learning Result for Metal Identification
Shinnosuke OkawaKunihisa TashiroHiroyuki WakiwakaYoshihiro NakamuraKazutoshi Machida
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2021 Volume 141 Issue 4 Pages 233-238

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Abstract

The purpose of this study is improvement of metal identification performance with step response. Feature values are maximum derivative current and its reaching time, these values depend on lift-off in the range of 0.5-1.5 mm. As a result of metal identifications, decision tree is the fastest and highest accuracy in 4 machine learning models. When increasing training samples, calculation time of all models are increasing, and accuracies are saturated 100 samples. When comparing between data whose lift-off is from 0.5 to 1.5 mm and data that fixed lift-off, classification accuracy in data fixed lift-off is improve than one in data not fixed lift-off.

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© 2021 by the Institute of Electrical Engineers of Japan
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