Best Resources for Imbalanced Classification

Classification is a predictive modeling problem that involves predicting a class label for a given example. It is generally assumed that the distribution of examples in the training dataset is even across all of the classes. In practice, this is rarely the case. Those classification predictive models where the distribution of examples across class labels is not equal (e.g. are skewed) are called "imbalanced classification." Typically, a slight imbalance is not a problem and standard machine learning techniques can be used. In those cases where the imbalance is severe, such as a 1:100, 1:1000, or higher ratio of the minority to the majority class, then specialized techniques are required.