Balanced random forest for imbalanced data streams

Data with highly imbalanced class distributions are common in real life. Machine learning application domains such as e-commerce, risk management, environmental, and health monitoring often suffer from class imbalance since the interesting case occurs rarely. Yet another layer of complexity is added when data arrives as massive streams. In such a setting, it is often of interest that a learning algorithm is updated in an incremental fashion for scalability and model adaptivity reasons while still handling the class imbalance. In this paper, we propose an ensemble algorithm for imbalanced data streams based on the offline balanced random forest idea. We also show on a recent dataset that the algorithm is useful for the buyer prediction problem in large-scale recommender systems.