Adaptive Learning with Covariate Shift-Detection for Non-Stationary Environments

Learning with data-set shift is a major challenge in non-stationary environments wherein the input data distribution may shift over time. Detecting the data-set shift point in the time-series data, where the distribution of time-series shifts its properties, is of utmost interest. Data-set shift exists in a broad range of real-world systems. In such systems, there is a need for continuous monitoring of the process behavior and tracking the state of the shift so as to decide about initiating adaptation in a timely manner. This paper presents an adaptive learning algorithm with data-set shift-detection using an exponential weighted moving average (EWMA) model based test in a non-stationary environment. The proposed method initiates the adaptation by re-configuring the knowledge-base of the classifier. This algorithm is suitable for real-time learning in non-stationary environments. Its performance is evaluated through experiments using synthetic data-sets. Results show that it reacts well to different co-variate shifts.