A Hybrid Learning Framework for Imbalanced Classification

Class imbalance is a well-known and challenging algorithmic research topic among the machine learning community as traditional classifiers generally perform poorly on imbalanced problems, where data to be learned have skewed distributions between their classes. This paper presents a hybrid framework named PRUSBoost for learning imbalanced classification. It combines a selective data under-sampling procedure and a powerful boosting strategy to effectively enhance classification performance on imbalanced problems. Different from the simple random under sampling algorithm, this framework constructs the training data of the majority or negative class by using a newly developed partition based under sampling approach. Experiments on several datasets from different application domains that carry skewed class distributions have shown that the proposed framework provides a very competitive, consistent, and effective solution to imbalanced classification problems.