Risk Evaluation of Overseas Mining Investment Based on a Support Vector Machine

Abstract

Analyzing the general method of establishing a support vector machine evaluation model, this paper discusses the application of this model in the risk assessment of overseas mining investment. Based on the analysis of the risk assessment index system of overseas mining investment, the related parameters of the optimal model were ascertained by training the sample data of 20 countries collected in 2015 and 2016, and the investment risk of 8 test samples was evaluated. All 8 samples were correctly identified, with an error rate of 0. South Africa’s mining investment risk in 2016 was assessed using the risk evaluation model for overseas mining investment based on a support vector machine, and it was rated as grade IV (general investment risk). The results show that the model can provide a new solution for the judgment and deconstruction of the risk of overseas mining investment.

Ocena ryzyka inwestycji w minerały w krajach przyjmujących w oparciu o model chmury materii i pierwiastka

“…At present, the main methods for evaluating the risk of overseas mining investment include the analytic hierarchy process (AHP), data envelopment analysis (DEA), artificial neural network (ANN) analysis, support vector machine (SVM), the fuzzy comprehensive evaluation method, the gray evaluation method, sensitivity analysis, the entropy method, the particle swarm algorithm and the BP neural network (Banda 2019;He et al 2021He et al , 2022Ke et al 2012;Khalili-Damghani et al 2016;Memon et al 2015).…”