Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection
Combining the outputs of various k-nearest neighbor anomaly detectors to form a robust ensemble model for high-dimensional geochemical anomaly detection
复制标题
结合各种 k 最近邻异常检测器的输出，形成用于高维地球化学异常检测的鲁棒集成模型
DOI：
10.1016/j.gexplo.2021.106875
复制
发表时间：
2021-08-26
影响因子：
3.9
通讯作者：
Lu, Laijun
中科院分区：
文献类型：
--
作者：
Chen, Yongliang;Zhao, Qingying;Lu, Laijun
机器学习技术为高维地球化学异常检测提供了有效的方法。然而，机器学习模型的不稳定性往往导致高维地球化学异常检测结果的不确定性。结合各种独立模型组成自适应集成异常检测器是提高机器学习异常检测器鲁棒性的可行途径。本文采用平均法、最大化法、最大均值法（AOM）和最大均值法（MOA）对不同k近邻（KNN）异常探测器的输出结果进行组合，提高KNN模型在吉林省白山区高维地球化学异常探测中的鲁棒性。通过将四种组合算法与单一KNN模型、高斯混合模型（GMM）、一类支持向量机（OCSVM）和隔离森林（ifforest）的集成模型进行对比，评价了四种组合算法在高维地球化学异常检测中的有效性。结果表明：(a)四种系综模型在高维地球化学异常检测中的表现相似，(b)在高维地球化学异常检测中的表现优于单一KNN模型、GMM、OCSVM和ifforest。因此，平均法、最大化法、AOM法和MOA法是将各种KNN模型的输出组合成鲁棒系综模型用于高维地球化学异常检测的潜在有用算法。
Machine learning techniques provide useful methods for high-dimensional geochemical anomaly detection for mineral exploration targeting. However, the instability of the machine learning models often leads to the uncertainty of high-dimensional geochemical anomaly detection result. Combining various individual models to form an adaptive ensemble anomaly detector is a feasible way to enhance the robustness of machine learning anomaly detectors. In this study, the average method, maximization method, average of maximum (AOM) method, and maximum of average (MOA) method were adopted to combine the outputs of various k-nearest neighbor (KNN) anomaly detectors to improve the robustness of the KNN models in the high-dimensional geochemical anomaly detection in the Baishan district (Jilin Province, China). The effectiveness of the four combination algorithms for high-dimensional geochemical anomaly detection was evaluated by comparing the ensemble models obtained by using the four combination algorithms with the single KNN model, Gaussian mixture model (GMM), one-class support vector machine (OCSVM), and isolation forest (IForest) in the case study. It is found that the four ensemble models (a) perform similarly well in high-dimensional geochemical anomaly detection, and (b) perform better than the single KNN model, GMM, OCSVM, and IForest in highdimensional geochemical anomaly detection. Therefore, the average method, maximization method, AOM method, and MOA method are potentially useful algorithms for combining the outputs of various KNN models to form robust ensemble models for high-dimensional geochemical anomaly detection.