scholarly journals A Multi-Model Ensemble Approach for Gold Mineral Prospectivity Mapping: A Case Study on the Beishan Region, Western China

Minerals ◽  
2020 ◽  
Vol 10 (12) ◽  
pp. 1126
Author(s):  
Kaijian Wang ◽  
Xinqi Zheng ◽  
Gongwen Wang ◽  
Dongya Liu ◽  
Ning Cui
Keyword(s):  
Machine Learning ◽  
Ensemble Learning ◽  
Learning Algorithm ◽  
Gold Deposits ◽  
Western China ◽  
Support Vector ◽  
Learning Methods ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Prospectivity Mapping

Mineral prospectivity mapping (MPM) needs robust predictive techniques so that the target zones of mineral deposits can be accurately delineated at a specific location. Although an individual machine learning algorithm has been successfully applied, it remains a challenge because of the complicated non-linear relations between prospecting factors and deposits. Ensemble learning methods were efficiently applied for their excellent generalization, but their potential has not been fully explored in MPM. In this study, three well-known machine learning models, namely random forest (RF), support vector machine (SVM), and the maximum entropy model (MaxEnt), were fused into ensembles (i.e., RF–SVM, RF–MaxEnt, SVM–MaxEnt, RF–SVM–MaxEnt) to produce a final prediction. The paper aims to investigate the potential application of stacking ensemble learning methods (SELM) for MPM. In this study, 69 hydrothermal gold deposits were split into two parts: 70% for the training model and 30% for testing the model. Then, 11 mineral prospecting factors were selected as a spatial dataset constructed for MPM. Finally, the models’ performance was assessed using the receiver operating characteristic (ROC) curves and five statistical metrics. Compared with other single methods, the SELM framework showed an improved predictive performance in the model evaluation. Therefore, this finding suggests that the SELM framework is promising and should be selected as an alternative technique for MPM.

scholarly journals Data-driven based logistic function and prediction-area plot for mineral prospectivity mapping: a case study from the eastern margin of Qinling orogenic belt, central China

2021 ◽  
Author(s):  
Hongyang Bai ◽  
Yuan Cao ◽  
Heng Zhang ◽  
Wenfeng Wang ◽  
Chaojun Jiang ◽  
...  
Keyword(s):  
Logistic Function ◽  
Gold Deposits ◽  
Orogenic Gold ◽  
Central China ◽  
Data Driven ◽  
Orogenic Gold Deposits ◽  
Mineral Prospectivity Mapping ◽  
Mineral Prospectivity ◽  
Potential Area ◽  
Prospectivity Mapping

Abstract he present work combines data-driven based logistic function with prediction-area plot for delineating target areas of orogenic gold deposits in eastern margin of Qinling metallogenic belt, central China. Firstly, the values of geological and geochemical information layer were transformed into a series of fuzzy numbers with a range of 0-1 through a data-driven based logistic function on the basis of mineralization theory of the orogenic gold deposits. Secondly, the prediction-area(P-A) plot was performed on the above evidence layers and their corresponding fuzzy overlay layers to pick out a proper prediction scheme for mineral prospectivity mapping(MPM) based on the known gold occurrences. What’s more, to further prove the advantages of this method, we also used a knowledge-driven approach for comparison purpose. Finally, with the concentration-area(C-A) fractal model, the fractal thresholds were determined and a mineral prospecting map was generated. The result, five of the six known gold deposits are located in high and moderate potential areas (accounts for 18.6 % of the study area), one in low potential area (accounts for 38.4 % of the study area) and none in weak potential area (accounts for 43 % of the study area), confirmed the joint application of data-driven based logistic function and prediction-area plot a simple, effective and low-cost method for mineral prospectivity mapping, which can be a guidance for further work in the research area.

scholarly journals A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping

Minerals ◽  
2019 ◽  
Vol 9 (5) ◽  
pp. 317 ◽  
Author(s):  
Yongliang Chen ◽  
Wei Wu ◽  
Qingying Zhao
Keyword(s):  
Bat Algorithm ◽  
Support Vector ◽  
Trial And Error ◽  
Optimal Parameters ◽  
Mineral Prospectivity Mapping ◽  
The Common ◽  
Mineral Prospectivity ◽  
Trial And Error Method ◽  
Error Method ◽  
Prospectivity Mapping

One-class support vector machine (OCSVM) is an efficient data-driven mineral prospectivity mapping model. Since the parameters of OCSVM directly affect the performance of the model, it is necessary to optimize the parameters of OCSVM in mineral prospectivity mapping. Trial and error method is usually used to determine the “optimal” parameters of OCSVM. However, it is difficult to find the globally optimal parameters by the trial and error method. By combining OCSVM with the bat algorithm, the intialization parameters of the OCSVM can be automatically optimized. The combined model is called bat-optimized OCSVM. In this model, the area under the curve (AUC) of OCSVM is taken as the fitness value of the objective function optimized by the bat algorithm, the value ranges of the initialization parameters of OCSVM are used to specify the search space of bat population, and the optimal parameters of OCSVM are automatically determined through the iterative search process of the bat algorithm. The bat-optimized OCSVMs were used to map mineral prospectivity of the Helong district, Jilin Province, China, and compared with the OCSVM initialized by the default parameters (i.e., common OCSVM) and the OCSVM optimized by trial and error. The results show that (a) the receiver operating characteristic (ROC) curve of the trial and error-optimized OCSVM is intersected with those of the bat-optimized OCSVMs and (b) the ROC curves of the optimized OCSVMs slightly dominate that of the common OCSVM in the ROC space. The area under the curves (AUCs) of the common and trial and error-optimized OCSVMs (0.8268 and 0.8566) are smaller than those of the bat-optimized ones (0.8649 and 0.8644). The optimal threshold for extracting mineral targets was determined by using the Youden index. The mineral targets predicted by the common and trial and error-optimized OCSVMs account for 29.61% and 18.66% of the study area respectively, and contain 93% and 86% of the known mineral deposits. The mineral targets predicted by the bat-optimized OCSVMs account for 19.84% and 14.22% of the study area respectively, and also contain 93% and 86% of the known mineral deposits. Therefore, we have 0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478, indicating that the bat-optimized OCSVMs perform slightly better than the common and trial and error-optimized OCSVMs in mineral prospectivity mapping.

Classification of P2P Traffic Based on a Heteromorphic Ensemble Learning Model

2014 ◽  
Vol 687-691 ◽  
pp. 2693-2697
Author(s):  
Li Ding ◽  
Li Mao ◽  
Xiao Feng Wang
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Ensemble Learning ◽  
Classification Accuracy ◽  
Learning Algorithm ◽  
Learning Model ◽  
Support Vector ◽  
Machine Learning Algorithm ◽  
Data Environment

One single machine learning algorithm presents shortcomings when the data environment changes in the process of application. This article puts forward a heteromorphic ensemble learning model made up of bayes, support vector machine (SVM) and decision tree which classifies P2P traffic by voting principle. The experiment shows that the model can significantly improve the classification accuracy, and has a good stability.

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