Masoud Yousefi, Khosrow Rahmani, Masoud Rajabi, Ali Reyhani, Mehdi Moudi
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引用次数: 0
摘要
随机森林（RF）算法被认为是一种强大的统计分类器，在其他领域较为流行，但在HEA的预测阶段却相对陌生。在这项研究中，随机森林（RF）技术被用来有效地研究阶段选择原则，利用了一项大型实验案例研究，研究了401个不同的HEA，包括174个（SS）、54个（IM）和173个（SS+IM）阶段。与 SVM 和 KNN 相比，拟议方法对 HEA 分类的准确率高出近 10%。此外，所提方法的精确度与 ANN 相似。实验结果表明了基于射频的诊断方法的有效性和可靠性。
Random forest classifier for high entropy alloys phase diagnosis
The random forest (RF) algorithm is considered as a powerful statistical classifier that is more popular in other fields but is relatively unknown in HEA(s)’s prediction phase. In this research, Random Forest (RF) technique is used to investigate phase selection principles effectively utilizing a large experimental case study on 401 distinct HEAs, comprising 174 \(SS\), 54 \(IM\), and 173 \(SS+IM\) phases. The accuracy of the proposed method is almost 10% higher than SVM and KNN for classifying HEA(s). Moreover, the precision of the proposed method is similar to ANN. Experimental results indicate the validity and reliability of the RF-based diagnosis method.
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