Online learning with minority class resampling

This paper considers using online binary classification for target detection where the goal is to identify signals of interest within a sequence of received signals generated by a shifting background. In this setting, we assume there is significant class imbalance (100:1 or greater), the sequence of examples is arbitrarily long and the distribution of the majority (negative) class is slowly time-varying. This setting is typical in detection and classification problems in which time-varying effects are caused by some combination of shifting channel characteristics and interferers that enter and exit the scene. We show empirically that the addition of caching and minority class oversampling to online learners improves the g-means performance under these conditions by compensating for class imbalance.