A positive and unlabeled learning algorithm for mineral prospectivity mapping

Application of supervised machine learning algorithms for mineral prospectivity mapping (MPM) requires positive and negative training samples. Typically, known mineral deposits are considered as positive training samples. However, the selection of negative training samples in the process of MPM is challenging. The one-class classification methods require positive and unlabeled samples or only positive samples; while without requiring negative training samples. In this study, the positive and unlabeled learning (PUL) algorithm was employed to produce a potential map for Fe polymetallic mineralization in southwestern Fujian province, China. This study first examined the sensitivity of the PUL algorithm to different training sets of labeled and unlabeled locations. The predictive results on 10 random unlabeled datasets confirm that PUL modeling with different training sets is reproducible and stable. In addition, the trained model provides a strong spatial correlation between the predictive variables and the locations of known mineral deposits. Finally, the performance of PUL1algorithm is compared to one-class support vector machine (OCSVM), artificial neural networks (ANN), and logistic regression (LR). Comparative results indicate that the PUL model can achieve a better performance in terms of both fitting-rate, prediction-rate, and AUC value compared with OCSVM, ANN and LR. The labelling efforts can be significantly reduced because the PUL algorithm requires only a small number of positive samples and utilizes unlabeled data in training.