GOS-IL: A Generalized Over-Sampling Based Online Imbalanced Learning Framework

Online imbalanced learning has two important characteristics: samples of one class (minority class) are under-represented in the data set and samples come to the learner online incrementally. Such a data set may pose several problems to the learner. First, it is impossible to determine the minority class beforehand as the learner has no complete view of the whole data. Second, the status of imbalance may change over time. To handle such a data set efficiently, we present here a dynamic and adaptive algorithm called Generalized Over-Sampling based Online Imbalanced Learning (GOS-IL) framework. The proposed algorithm works by updating a base learner incrementally. This update is triggered when number of errors made by the learner crosses a threshold value. This deferred update helps the learner to avoid instantaneous harms of noisy samples and to achieve better generalization ability in the long run. In addition, correctly classified samples are not used by the algorithm to update the learner for avoiding over-fitting. Simulation results on some artificial and real world datasets show the effectiveness of the proposed method on two performance metrics: recall and g-mean.