GLOBAL GEOLOGY 2017, 20(2) 118-130 DOI:   10.3969/j.issn.1673-9736.2017.02.07  ISSN: 1673-9736 CN: 22-1371/P

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WU Wei1, CHEN Yongliang2
Cumulative gain and lift charts for model performance assessment in mineral potential mapping
WU Wei1, CHEN Yongliang2
1. Changchun Institute of Urban Planning and Design, Changchun 130033, China;
2. Institute of Mineral Resources Prognosis on Synthetic Information, Jilin University, Changchun 130026, China

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.

Keywords: cumulative gain and lift charts   mineral potential mapping performance assessment   weights of evidence   logistic regression   restricted boltzmann machine   multilayer perceptron  
收稿日期 2015-12-05 修回日期 2015-12-20 网络版发布日期  
DOI: 10.3969/j.issn.1673-9736.2017.02.07

通讯作者: CHEN Yongliang, E-mail:


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