Adaptive learning with covariate shift-detection for non-stationary environments

• Published in: UK Workshop on Computational Intelligence pp. 1 - 8
• Main Authors: Raza, Haider, Prasad, Girijesh, Yuhua Li
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.09.2014
• Subjects: Adaptation models; adaptive learning; Adaptive systems; Classification algorithms; covaraite shift; dataset shif-detection; EWMA; Knowledge based systems; Monitoring; Non-stationary learning; Testing; Training
• ISSN: 2162-7657
• DOI: 10.1109/UKCI.2014.6930161

Learning with dataset shift is a major challenge in non-stationary environments wherein the input data distribution may shift over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series shifts its properties, is of utmost interest. Dataset 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 dataset shift-detection using an exponential weighted moving average (EWMA) model based test in a non-stationary environment. The proposed method initiates the adaptation by reconfiguring 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 datasets. Results show that it reacts well to different covariate shifts.