Title: A Dynamic Boosted Ensemble Learning Method Based on Random Forest,
Abstract: We propose a dynamic boosted ensemble learning method based on random forest
(DBRF), a novel ensemble algorithm that incorporates the notion of hard example
mining into Random Forest (RF) and thus combines the high accuracy of Boosting
algorithm with the strong generalization of Bagging algorithm. Specifically, we
propose to measure the quality of each leaf node of every decision tree in the
random forest to determine hard examples. By iteratively training and then
removing easy examples from training data, we evolve the random forest to focus
on hard examples dynamically so as to learn decision boundaries better. Data
can be cascaded through these random forests learned in each iteration in
sequence to generate predictions, thus making RF deep. We also propose to use
evolution mechanism and smart iteration mechanism to improve the performance of
the model. DBRF outperforms RF on three UCI datasets and achieved
state-of-the-art results compared to other deep models. Moreover, we show that
DBRF is also a new way of sampling and can be very useful when learning from
imbalanced data.