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A Comparative Study of Different Machine Learning Algorithms in Predicting the Content of Ilmenite in Titanium Placer
2020
Applied Sciences
In this study, the ilmenite content in beach placer sand was estimated using seven soft computing techniques, namely random forest (RF), artificial neural network (ANN), k-nearest neighbors (kNN), cubist, support vector machine (SVM), stochastic gradient boosting (SGB), and classification and regression tree (CART). The 405 beach placer borehole samples were collected from Southern Suoi Nhum deposit, Binh Thuan province, Vietnam, to test the feasibility of these soft computing techniques in
doi:10.3390/app10020635
fatcat:gmuuo4uhy5bbvbvzuuzh7nfsha