Abstract
Random forests have been used as effective models to tackle a number of classification and regression problems. In this paper, we present a new type of Random Forests (RFs) called Red(uced)-RF that adopts a new voting mechanism called Priority Vote Weighting (PV) and a new dynamic data reduction principle which improve accuracy and execution time compared to Breiman's conventional RF. Red-RF also shows that the strength of a random forest can increase without noticeably increasing correlation between the trees. We then compare performance of Red-RF, 9 new RF variants and Breiman's RF in eight experiments that involve classification problems with datasets of different sizes.
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