Raza, Haider, Prasad, Girijesh and Li, Yuhua  ORCID: https://orcid.org/0000-0003-2913-4478
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
|
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 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: | Schools > Computer Science & Informatics |
| Publisher: | Springer |
| ISBN: | 978-3-642-41141-0 |
| ISSN: | 1868-4238 |
| Last Modified: | 07 Nov 2022 09:26 |
| URI: | https://orca.cardiff.ac.uk/id/eprint/129152 |
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