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http://dx.doi.org/10.12656/jksht.2021.34.5.211

Development of Machine Learning Ensemble Model using Artificial Intelligence  

Lee, K.W. (School of Advanced Materials Engineering, Kookmin University)
Won, Y.J. (School of Advanced Materials Engineering, Kookmin University)
Song, Y.B. (Agency for Defense Development)
Cho, K.S. (School of Advanced Materials Engineering, Kookmin University)
Publication Information
Journal of the Korean Society for Heat Treatment / v.34, no.5, 2021 , pp. 211-217 More about this Journal
Abstract
To predict mechanical properties of secondary hardening martensitic steels, a machine learning ensemble model was established. Based on ANN(Artificial Neural Network) architecture, some kinds of methods was considered to optimize the model. In particular, interaction features, which can reflect interactions between chemical compositions and processing conditions of real alloy system, was considered by means of feature engineering, and then K-Fold cross validation coupled with bagging ensemble were investigated to reduce R2_score and a factor indicating average learning errors owing to biased experimental database.
Keywords
Machine learning; Cross validation; Bagging ensemble; Feature engineering;
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