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An evaluation of Guided Regularized Random Forest for classification and regression tasks in remote sensing
2020
International Journal of Applied Earth Observation and Geoinformation
A B S T R A C T New Earth observation missions and technologies are delivering large amounts of data. Processing this data requires developing and evaluating novel dimensionality reduction approaches to identify the most informative features for classification and regression tasks. Here we present an exhaustive evaluation of Guided Regularized Random Forest (GRRF), a feature selection method based on Random Forest. GRRF does not require fixing a priori the number of features to be selected or
doi:10.1016/j.jag.2020.102051
fatcat:cshllfratvfrfdyep47y2o3whm