A comparative study of the cost–benefit strategy with the learning ensembles of decision stumps in polymetallic prospectivity modelling

The prospecting cost–benefit strategy is an easy-to-implement mineral prospectivity modeling algorithm which uses the likelihood ratio, lift index and Youden index to represent the mineral potential. In this study, the prospecting cost–benefit strategy was extended to use the Matthews correlation coefficient (MCC) and F-measure to represent the mineral potential, and compared with the bagging and boosting ensembles of decision stumps in polymetallic prospectivity modeling in the Lalingzaohuo district (Qinghai Province, China). Replacing the decision trees with decision stumps in the bagging algorithm can alleviate the overfitting problem of the bagging ensemble model caused by the depth of the decision trees. According to the polymetallic prospectivity modeling results of the extended prospecting cost–benefit strategy, and bagging and boosting ensembles, seven mineral potential maps were produced, including likelihood ratio map, lift index map, Youden index map, MCC map, F-measure map, classification score maps. The receiver operating characteristic (ROC) curves show that the prospecting cost–benefit strategy is superior to the ensemble learning models in polymetallic prospectivity modeling. According to the seven mineral potential maps, polymetallic prospective areas were optimally delineated in the study area. These polymetallic prospective areas account for only a small percentage of the whole study area (12.61 – 16.95%) but contain almost all known polymetallic deposits (94 – 100%). Therefore, the MCC and F-measure can be used to represent the mineral potential in the prospecting cost–benefit strategy. The extended prospecting cost–benefit strategy performs better than the ensemble learning algorithms in polymetallic prospectivity modeling.