Imbalanced Datasets: from Sampling to Classifiers

Classification is one of the most fundamental tasks in the machine learning and data-mining communities. One of the most common challenges faced when trying to perform classification is the class imbalance problem. A dataset is considered imbalanced if the class of interest (positive or minority class) is relatively rare as compared to the other classes (negative or majority classes). As a result, the classifier can be heavily biased toward the majority class. A number of sampling approaches, ranging from under-sampling to over-sampling, have been developed to solve the problem of class imbalance. One challenge with sampling strategies is deciding how much to sample, which is obviously conditioned on the sampling strategy that is deployed. While a wrapper approach may be used to discover the sampling strategy and amounts, it can quickly become computationally prohibitive. To that end, recent research has also focused on developing novel classification algorithms that are class imbalance (skew) insensitive. In this chapter, we provide an overview of the sampling strategies as well as classification algorithms developed for countering class imbalance. In addition, we consider the issues of correctly evaluating the performance of a classifier on imbalanced datasets and present a discussion on various metrics.