GPU-Accelerated Extreme Learning Machines for Imbalanced Data Streams with Concept Drift

Mining data streams is one of the most vital fields in the current era of big data. Continuously arriving data may pose various problems, connected to their volume, variety or velocity. In this paper we focus on two important difficulties embedded in the nature of data streams: non-stationary nature and skewed class distributions. Such a scenario requires a classifier that is able to rapidly adapt itself to concept drift and displays robustness to class imbalance problem. We propose to use online version of Extreme Learning Machine that is enhanced by an efficient drift detector and method to alleviate the bias towards the majority class. We investigate three approaches based on undersampling, oversampling and cost-sensitive adaptation. Additionally, to allow for a rapid updating of the proposed classifier we show how to implement online Extreme Learning Machines with the usage of GPU. The proposed approach allows for a highly efficient mining of high-speed, drifting and imbalanced data streams with significant acceleration offered by GPU processing.