PDF(5087 KB)
Cumulative gain and lift charts for model performance assessment in mineral potential mapping
WU Wei, CHEN Yongliang
Global Geology ›› 2017, Vol. 20 ›› Issue (2) : 118-130.
PDF(5087 KB)
PDF(5087 KB)
Cumulative gain and lift charts for model performance assessment in mineral potential mapping
Model performance assessment is a key procedure for mineral potential mapping, but the corresponding research achievements are seldom reported in literature. Cumulative gain and lift charts are well known in the data mining community specialized in marketing and sales applications and widely used in customer churn prediction for model performance assessment. In this paper, they are introduced into the field of mineral potential mapping for model performance assessment. These two charts can be viewed as a graphic representation of the advantage of using a predictive model to choose mineral targets. A cumulative gain curve can represent how much a predictive model is superior to a random guess in mineral target prediction. A lift chart can express how much more likely the mineral targets predicted by a model are deposit-bearing ones than those by a random selection. As an illustration, the cumulative gain and lift charts are applied to measure the performance of weights of evidence, logistic regression, restricted Boltzmann machine, and multilayer perceptron in mineral potential mapping in the Altay district in northern Xinjiang in China. The results show that the cumulative gain and lift charts can visually reveal that the first three models perform well while the last one performs poorly. Thus, the cumulative gain and lift charts can serve as a graphic tool for model performance assessment in mineral potential mapping.
cumulative gain and lift charts / mineral potential mapping performance assessment / weights of evidence / logistic regression / restricted boltzmann machine / multilayer perceptron
Agterberg F P. 1974. Automatic contouring of geological maps to detect target areas for mineral exploration. Mathematical Geology, 6(4): 373-395.
Agterberg F P. 1989. LOGDIA-FORTRAN 77 program for logistic regression with diagnostics. Computers & Geosciences, 15(4): 599-614.
Agterberg F P. 1990. Combining indicator patterns for mineral resource evaluation//China University of Geosciences (eds.). Proceedings of International Workshop on Statistical Prediction of Mineral Resources. Wuhan: 1-15.
Agterberg F P. 1992. Combining indicator patterns in weights of evidence modeling for resource evaluation. Nonrenewable Resources, 1(1): 39-50.
Agterberg F P, Bonham-Carter G F, Wright D F. 1990. Statistical pattern integration for mineral exploration//Gaal G, Merriam D F. (eds). Computer applications for mineral exploration in resource exploration. Oxford: Pergamon Press, 1-21.
Agterberg F P, Bonham-Carter G F. 1999. Logistic regression and weights of evidence modeling in mineral exploration//Proceedings of the 28th International Symposium on Applications of Computer in the Mineral Industry (APCOM), Golden, Colorado, 483-490.
Agterberg F P, Cheng Q M. 2002. Conditional independence test for weights-of-evidence modeling. Natural Resources Research, 11(4): 249-255.
Anjum S. 2014. Composite indicators for data mining: A new framework for assessment of prediction classifiers. Journal of Economics, Business and Management, 2(1): 62-67.
Bekkar M, Djemaa H K, Alitouche T A. 2013. Evaluation measures for models assessment over imbalanced data sets. Journal of Information Engineering and Applications, 3(10): 27-38.
Bergmann R, Ludbrook J, Spooren W P J M. 2000. Different outcomes of the Wilcoxon-Mann-Whitney test from different statistics packages. The American Statistician, 54(1): 72-77.
Berry M J A, Linoff G. 1999. Data mining techniques: for marketing, sales, and customer support. New York: John Wiley & Sons.
Bonham-Carter G F, Agterberg F P, Wright D F. 1988. Integration of geological datasets for gold exploration in Nova Scotia. Photogrammetric Engineering and Remote Sensing, 54(11): 1585-1592.
Bonham-Carter G F, Agterberg F P, Wright D F. 1989. Weights-of-evidence modelling: a new approach to mapping mineral potential//Agterberg, F P, Bonham-Carter G F. (Eds.). Statistical applications in the earth sciences. Geological Survey of Canada, 171-183.
Burez J, Van den Poel D. 2009. Handling class imbalance in customer churn prediction. Expert Systems with Applications, 36: 4626-4636.
Carranza E J M, Hale M. 2001. Logistic regression for geologically constrained mapping of gold potential, Baguio district, Philippines. Exploration and Mining Geology, 10: 165-175.
Carranza E J M, Hale M. 2002a. Where are porphyry copper deposits spatially localized? A case study in Benguet province, Philippines. Natural Resources Research, 11: 45-59.
Carranza E J M, Hale M. 2002b. Wildcat mapping of gold potential, Baguio district, Philippines. Transactions Institute of Mining and Metallurgy (Applied Earth Science), 111: 100-105.
Chen C H, Dai H Z, Liu Y, et al. 2011. Mineral prospectivity mapping integrating multisource geology spatial data sets and logistic regression modeling//Proceedings of IEEE International Conference on Spatial Data Mining and Geographical Knowledge Services (ICSDM), 214-217.
Chen Y L. 2015. Mineral potential mapping with a restricted Boltzmann machine. Ore Geology Reviews, 71: 749-760.
Chen Y L, Lu L J, Li X B. 2014. Application of continuous restricted Boltzmann machine to identify multivariate geochemical anomaly. Journal of Geochemical Exploration, 140: 56-63.
Flach P A, Hernandez-Orallo J, Ferri C. 2011.//Proceedings of the 28th International Conference on Machine Learning (ICML-11), Bellevue, WA, USA, 657-664.
Japkowicz N. 2000. The class imbalance problem: Significance and strategies//Proceedings of the 2000 international conference on artificial intelligence (IC-AI'2000): Special track on inductive learning, Las Vegas, Nevada.
Li Z C. 1996. The evolution and formation of the left echelon volcanic-sedimentary basins in Altay and the tracing of their metallogenic activities. Geotectonica et Metallogenia, 20(3): 189-200. (in Chinese with English Abstract)
Li Z C, Zhao Z H. 2002. Creation of the Altay orogenic belt and the Altay MTS. Tectono-metallogenic province. Chinese Journal of Geology, 37(4): 483-490. (in Chinese with English Abstract)
Nykänen V, Groves D I, Ojala V J, et al. 2008. Combined conceptual/empirical prospectivity mapping for orogenic gold in the northern Fennoscandian Shield, Finland. Australian Journal of Earth Sciences, 55(1): 39-59.
Piatetsky-Shapiro G, Steingold S. 2000. Measuring lift quality in database marketing. ACM SIGKDD Explorations Newsletter, 2(2): 76-80.
Skabar A. 2007. Mineral potential mapping using Bayesian learning for multilayer perceptrons. Mathematical Geology, 39(5): 439-451.
Tangestani M H, Moore F. 2001. Porphyry copper potential mapping using the weights-of-evidence model in a GIS, northern Shahr-e-Babak, Iran. Australian Journal of Earth Sciences, 48(5): 913-927.
Tuffery S. 2005. Data mining et statistique decisionnelle. Paris: Edition TECHNIP.
Verbeke W, Martens D, Mues C, et al. 2011. Building comprehensible customer churn prediction models with advanced rule induction techniques. Expert Systems with Applications, 38: 2354-2364.
Xie Y, Li X, Ngai E W T, et al. 2009. Customer churn prediction using improved balanced random forests. Expert Systems with Applications, 36: 5445-5449.
Xu S, Cui Z K, Yang X L, et al. 1992. A preliminary application of weights of evidence in gold exploration in Xionger mountain region, Henan province. Mathematical Geology, 24(6): 663-674.
Zeng Q S, Chen G H, Wang H, et al. 2005. Polygenetic compound metallogenic characteristics of the Ashele copper deposit in northern Xinjiang. Geotectonica et Metallogenica, 29: 545-550. (in Chinese with English Abstract)
Supported by Project of the National Natural Science Foundation of China (Nos.41272360, 41472299, 61133011)
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