An Empirical Study of Random Forests for Mining Imbalanced Data

Skewed or imbalanced data presents a significant problem for many standard learners which focus on optimizing the overall classification accuracy. When the class distribution is skewed, priority is given to classifying examples from the majority class, at the expense of the often more important minority class. The random forest (RF) classification algorithm, which is a relatively new learner with appealing theoretical properties, has received almost no attention in the context of skewed datasets. This work presents a comprehensive suite of experimentation evaluating the effectiveness of random forests for learning from imbalanced data. Reasonable parameter settings (for the Weka implementation) for ensemble size and number of random features selected are determined through experimentation oil 10 datasets. Further, the application of seven different data sampling techniques that are common methods for handling imbalanced data, in conjunction with RF, is also assessed. Finally, RF is benchmarked against 10 other commonly-used machine learning algorithms, and is shown to provide very strong performance. A total of 35 imbalanced datasets are used, and over one million classifiers are constructed in this work.