A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping
A Bat-Optimized One-Class Support Vector Machine for Mineral Prospectivity Mapping
复制标题
用于矿产远景测绘的蝙蝠优化一级支持向量机
DOI：
10.3390/min9050317
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发表时间：
2019-05-01
期刊：
影响因子：
2.5
通讯作者：
Zhao, Qingying
中科院分区：
文献类型：
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作者：
Chen, Yongliang;Wu, Wei;Zhao, Qingying
单类支持向量机（OCSVM）是一种有效的数据驱动的矿产预测模型。由于OCSVM的参数直接影响模型的性能，因此在矿产远景图的编制中有必要对OCSVM的参数进行优化。通常采用试凑法来确定OCSVM的“最优”参数。然而，很难找到全局最优的参数的尝试和错误的方法。将OCSVM与蝙蝠算法相结合，可以自动优化OCSVM的初始化参数。这种组合模型被称为蝙蝠优化的OCSVM。该模型以OCSVM的曲线下面积（AUC）作为蝙蝠算法优化目标函数的适应度值，以OCSVM初始参数的取值范围来指定蝙蝠种群的搜索空间，通过蝙蝠算法的迭代搜索过程自动确定OCSVM的最优参数.将蝙蝠优化的OCSVM用于中国吉林省和龙地区的矿产远景图，并与由默认参数（即，普通OCSVM）和通过试错优化的OCSVM。结果表明：（a）试错优化OCSVM的受试者工作特征（ROC）曲线与蝙蝠优化OCSVM的受试者工作特征（ROC）曲线重合;（B）优化OCSVM的ROC曲线在ROC空间上略优于普通OCSVM。普通和试验和误差优化的OCSVM的曲线下面积（AUC）（0.8268和0.8566）小于蝙蝠优化的OCSVM的曲线下面积（0.8649和0.8644）。利用Youden指数确定了提取矿物目标的最佳阈值。普通和试差优化OCSVM预测的找矿靶区分别占研究区面积的29.61%和18.66%，包含了已知矿床的93%和86%。蝙蝠优化的OCSVM预测的矿产目标分别占研究面积的19.84%和14.22%，也包含了93%和86%的已知矿床。因此，我们有0.93/0.2961 = 3.1408 < 0.86/0.1866 = 4.6088 < 0.93/0.1984 = 4.6875 < 0.86/0.1422 = 6.0478，表明蝙蝠优化的OCSVM在矿产远景图中的表现略好于普通和试错优化的OCSVM。
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.