A Dynamic Boosted Ensemble Learning Method Based on Random Forest
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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 and noise 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 unbalanced data.
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