Donald E Brown

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Analysis of robust measures in Random Forest Regression (RFR) is an extensive empirical analysis on a new method, Robust Random Forest Regression (RRFR). The application and analysis of this tree-based method has yet to be addressed and may provide additional insight in modeling complex data. Our approach is based on the RFR with two major differences ~ the(More)
Improving the Robust Random Forest Regression (RRFR) Algorithm leads to the discovery of a new forest prediction method called Booming. In previous research, we determined that RRFR was more robust than Random Forest Regression (RFR) to unbounded outliers and heteroscedastic datasets using a DFfits style analysis; however, with other dirty datasets RFR(More)
A comparative analysis of two forest-based regression algorithms is an in-depth investigation of the merits of Random Forest Regression (RFR) and Robust Random Forest Regression (RRFR). In previous research, we determined that RRFR was more robust than RFR to unbounded outliers and heteroscedastic datasets using a DFfits style analysis. The study's goal is(More)
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