Concept Drift Detection and Adaptation with Hierarchical Hypothesis Testing

Effective techniques for analyzing and detecting changes in streaming data, especially in the era of big data, pose new challenges to the machine learning and the statistics community [1], [2]. As a result, early approaches for detecting statistical changes in a time series (such as change point detection), have had to be extended for online detection of changes in a multivariate data streams [3], [4]. Some of these techniques for detecting the intrinsic change in the relationship of the incoming data streams have been applied to numerous real-world applications, such as fraud detection, user preference prediction and email filtering, [5], [6]. Online classification is another common task performed on streaming multivariate time series data that takes advantage of these statistical relationships to predict a class label at each time index [7]. If the underlying source generating the data is not stationary, the optimal decision rule for the classifier would change over time - a phenomena known as concept drift [8]. Given the impact of concept drift on the predictive performance of an online classifier, there is a need to detect these concept drifts as early as possible. The inability of change point detection approaches to detect these concept drifts, has motivated the need for concept drift detection approaches that not only monitor the join distribution of a multivariate data stream but also changes in its relationship to the class labels of the streaming data. Shujian Yu and José C. Príncipe are with the Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA.