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

Raza, H, Prasad, G and Li, Y 2013, 'EWMA based two-stage dataset shift-detection in non-stationary environments' , in: Artificial Intelligence Applications and Innovations : 9th IFIP WG 12.5 International Conference, AIAI 2013, Paphos, Cyprus, September 30–October 2, 2013, Proceedings , IFIP Advances in Information and Communication Technology (412) , Springer Berlin Heidelberg, London, pp. 625-635.

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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.

Item Type: Book Section
Editors: Papadopoulos, H, Andreou, AS, Iliadis, L and Maglogiannis, I
Schools: Schools > School of Computing, Science and Engineering
Publisher: Springer Berlin Heidelberg
Refereed: Yes
Series Name: IFIP Advances in Information and Communication Technology
ISBN: 9783642411427
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 08 Jul 2015 12:31
Last Modified: 05 Apr 2016 18:18
URI: http://usir.salford.ac.uk/id/eprint/33111

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