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EWMA based two-stage dataset shift-detection in non-stationary environments

Raza, Haider, Prasad, Girijesh and Li, Yuhua ORCID: 2013. EWMA based two-stage dataset shift-detection in non-stationary environments. Presented at: AIAI 2013, Paphos, Cyprus, 30 Sep - 2 Oct 2013. Published in: Papadopoulos, Harris, Andreou, Andreas S., Iliadis, Lazaros and Maglogiannis, Ilias eds. Artificial Intelligence Applications and Innovations. IFIP Advances in Information and Communication Technology , vol.412 Berlin, Heidelberg: Springer, pp. 625-635. 10.1007/978-3-642-41142-7_63

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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 Weighted Moving 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: Conference or Workshop Item (Paper)
Date Type: Publication
Status: Published
Schools: Computer Science & Informatics
Publisher: Springer
ISBN: 978-3-642-41141-0
ISSN: 1868-4238
Last Modified: 07 Nov 2022 09:26

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