Comparison of the Data-Driven Random Forests Model and a Knowledge-Driven Method for Mineral Prospectivity Mapping: A Case Study for Gold Deposits Around the Huritz Group and Nueltin Suite, Nunavut, Canada

Random forest predictive modeling of mineral prospectivity with small number of prospects and data with missing values in Abra (Philippines)

(2015) Emmanuel John M. Carranza et al.   COMPUTERS & GEOSCIENCES

Predictive modelling of gold potential with the integration of multisource information based on random forest: a case study on the Rodalquilar area, Southern Spain

(2014) V.F. Rodriguez-Galiano et al.   INTERNATIONAL JOURNAL OF GEOGRAPHICAL INFORMATION SCIENCE

A study of the lake sediment geochemistry of the Melville Peninsula using multivariate methods: Applications for predictive geological mapping

(2013) E.C. Grunsky et al.   JOURNAL OF GEOCHEMICAL EXPLORATION

Support vector machine for multi-classification of mineral prospectivity areas

(2012) Maysam Abedi et al.   COMPUTERS & GEOSCIENCES

Letter to the Editor: Stability of Random Forest importance measures

(2010) M. L. Calle et al.   BRIEFINGS IN BIOINFORMATICS

Support vector machine: A tool for mapping mineral prospectivity

(2010) Renguang Zuo et al.   COMPUTERS & GEOSCIENCES

A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data

(2009) Bjoern H Menze et al.   BMC BIOINFORMATICS

Classifier ensembles for land cover mapping using multitemporal SAR imagery

(2009) Björn Waske et al.   ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING