RUSBoost: Improving classification performance when training data is skewed
Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, Amri Napolitano · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · 2008
Constructing classification models using skewed training data can be a challenging task. We present RUSBoost, a new algorithm for alleviating the problem of class imbalance. RUSBoost combines data sampling and boosting, providing a simple and efficient method for improving classification performance when training data is imbalanced. In addition to performing favorably when compared to SMOTEBoost (another hybrid sampling/boosting algorithm), RUSBoost is computationally less expensive than SMOTEBoost and results in significantly shorter model training times. This combination of simplicity, speed and performance makes RUSBoost an excellent technique for learning from imbalanced data.