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EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments

Raza, Haider, Prasad, Girijesh and Li, Yuhua (2013) EWMA Based Two-Stage Dataset Shift-Detection in Non-stationary Environments. In: Artificial Intelligence Applications and Innovations. (Eds: Papadopoulos, Harris, Andreou, Andreas. S., IIiadis, Lazaros and Maglogiannis, IIias), Springer, London, pp. 625-635. ISBN 978-3-642-41141-0 [Book section]

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URL: http://link.springer.com/chapter/10.1007%2F978-3-642-41142-7_63

DOI: 10.1007/978-3-642-41142-7_63


Dataset shift is a major challenge in the non-stationary environments wherein the input data distribution may change over time. In a time-series data, detecting the dataset shift point, where the distribution changes 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 adaptive corrections in a timely manner. This paper presents a novel method to detect the shift-point based on a two-stage structure involving Exponentially WeightedMoving Average (EWMA) chart and Kolmogorov-Smirnov test, which substantially reduces type-I error rate. The algorithm is suitable to be run in real-time. Its performance is evaluated through experiments using synthetic and real-world datasets. Results show effectiveness of the proposed approach in terms of decreased type-I error and tolerable increase in detection time delay.

Item Type:Book section
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:28714
Deposited By: Professor Girijesh Prasad
Deposited On:25 Feb 2014 12:22
Last Modified:25 Feb 2014 12:22

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