A Novel Class Imbalance Learning Using Intelligent Under-Sampling
Class imbalance is a problem that is very much critical in many real-world application domains of machine learning. When examples of one class in a training data set vastly outnumber examples of the other class (es), traditional data mining algorithms tend to create suboptimal classification models. Researchers have rigorously studied several techniques to alleviate the problem of class imbalance, including resampling algorithms, and feature selection approaches to this problem. In this paper, the authors present a new hybrid feature selection algorithm dubbed as Class Imbalance Learning using Intelligent Under Sampling (CILIUS), for learning from skewed training data.