Online-Offline Extreme Learning Machine with Concept Drift Tracking for Time Series Data

Abstract

Data stream mining has been receiving increased attention due to its presence in a wide range of applications, such as sensor networks, banking, and telecommunication. One of the most important challenges during learning from data streams is reacting to concept drift, i.e. unforeseen changes of the stream's underlying data distribution. Traditional methods always used online learning to handle the concept drift problem. However, online learning requires high time cost during online training. To overcome this shortcoming, this paper proposes a Kalman filtering approach, which can provide robust concept drift detection, to track concept drift. Once concept drift happens, the online extreme learning machine is applied to update the tracking model, whereas the offline extreme learning machine is used when no concept drift occurs. Based on this idea, we propose a fusion framework to combine online and offline extreme learning machine to efficiently track the data stream. The experiment results indicate the superior performance of our method.