H Raza
EWMA based two-stage dataset shift-detection in non-stationary environments
Raza, H; Prasad, G; Li, Y
Authors
G Prasad
Y Li
Contributors
H Papadopoulos
Editor
AS Andreou
Editor
L Iliadis
Editor
I Maglogiannis
Editor
Abstract
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.
Citation
Raza, H., Prasad, G., & Li, Y. (2013). EWMA based two-stage dataset shift-detection in non-stationary environments. In H. Papadopoulos, A. Andreou, L. Iliadis, & I. Maglogiannis (Eds.), Artificial Intelligence Applications and Innovations : 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30–October 2, 2013, Proceedings (625-635). London: Springer Berlin Heidelberg. https://doi.org/10.1007/978-3-642-41142-7_63
Publication Date | Jan 1, 2013 |
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Deposit Date | Jul 8, 2015 |
Pages | 625-635 |
Series Title | IFIP Advances in Information and Communication Technology |
Series Number | 412 |
Book Title | Artificial Intelligence Applications and Innovations : 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30–October 2, 2013, Proceedings |
ISBN | 9783642411427 |
DOI | https://doi.org/10.1007/978-3-642-41142-7_63 |
Publisher URL | http://dx.doi.org/10.1007/978-3-642-41142-7_63 |
Related Public URLs | http://link.springer.com/book/10.1007/978-3-642-41142-7 |
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