Random Forest

Random Forests have been used as effective ensemble models for classification. We present in this paper a new type of Random Forests (RFs) called Red(uced)-RF that adopts a new dynamic data reduction principle and a new voting mechanism called Priority Vote Weighting (PV) which improve accuracy, execution time and AUC values compared to Breiman’s 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 and Breiman’s RF in 8 experiments that involve classification problems with datasets of different sizes. Finally, we conduct two additional experiments that involve considerably larger datasets with one million points in each.

Hasan Kurban
Hasan Kurban
Computer & Data Scientist

I’m a computer scientist & machine learning researcher who loves building intelligent systems to find data-driven solutions to real-world problems.