Deep Dynamic Boosted Forest
This addresses the problem of imbalanced data for machine learning practitioners, offering an incremental improvement over existing ensemble methods.
The paper tackles the challenge of learning from imbalanced data by proposing Deep Dynamic Boosted Forest (DDBF), which integrates hard example mining into random forests to dynamically focus on difficult samples, achieving state-of-the-art results on datasets like MNIST and outperforming random forest on 5 UCI datasets.
Random forest is widely exploited as an ensemble learning method. In many practical applications, however, there is still a significant challenge to learn from imbalanced data. To alleviate this limitation, we propose a deep dynamic boosted forest (DDBF), a novel ensemble algorithm that incorporates the notion of hard example mining into random forest. Specically, 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 balance the proportion of samples and learn decision boundaries better. Data can be cascaded through these random forests learned in each iteration in sequence to generate more accurate predictions. Our DDBF outperforms random forest on 5 UCI datasets, MNIST and SATIMAGE, and achieved state-of-the-art results compared to other deep models. Moreover, we show that DDBF is also a new way of sampling and can be very useful and efficient when learning from imbalanced data.