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

Yongliang Chen,Wei Wu,Qingying Zhao

doi:10.3390/min9050317

Yongliang Chen, Wei Wu + Show 1 more

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https://doi.org/10.3390/min9050317

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Abstract

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.

Highlights

One-class support vector machine (OCSVM) is an extended version of the support vector machine, which performs anomaly detection by modeling high-dimensional unlabeled data [1,2]
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 receiver operating characteristic (ROC) curve and area under the curve (AUC) value were used to evaluate whether the anomaly scores generated by the OCSVM model in Section 3.3 are effective for predicting known mineral deposit locations in the study area

Summary

Introduction

One-class support vector machine (OCSVM) is an extended version of the support vector machine, which performs anomaly detection by modeling high-dimensional unlabeled data [1,2]. This method has high performance and efficiency in identifying anomalies from high-dimensional data of unknown population distribution and has been successfully applied in many research fields. Mahadevan and Shah established an OCSVM model to detect faults from process data in control systems [6]. Harrou et al established an OCSVM model to detect anomalies in photovoltaic systems [12]. Chen and Wu applied OCSVM to mineral prospectivity mapping and geochemical anomaly detection [13,14]

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