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Adaptive learning with covariate shift-detection for non-stationary environments

Raza, H, Prasad, G and Li, Y 2014, Adaptive learning with covariate shift-detection for non-stationary environments , in: 14th UK Workshop on Computational Intelligence (UKCI2014), 8-10 September 2014, Bradford, England.

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Abstract

Learning with dataset shift is a major challenge in non-stationary environments wherein the input data distribution may shift over time. Detecting the dataset shift point in the time-series data, where the distribution of time-series shifts 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 adaptation in a timely manner. This paper presents an adaptive learning algorithm with dataset shift-detection using an exponential weighted moving average (EWMA) model based test in a non-stationary environment. The proposed method initiates the adaptation by reconfiguring the knowledge-base of the classifier. This algorithm is suitable for real-time learning in non-stationary environments. Its performance is evaluated through experiments using synthetic datasets. Results show that it reacts well to different covariate shifts.

Item Type: Conference or Workshop Item (Paper)
Schools: Schools > School of Computing, Science and Engineering
Journal or Publication Title: Computational Intelligence (UKCI), 2014 14th UK Workshop
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Refereed: Yes
Related URLs:
Funders: Non funded research
Depositing User: Yuhua Li
Date Deposited: 19 Jun 2015 18:27
Last Modified: 05 Apr 2016 19:29
URI: http://usir.salford.ac.uk/id/eprint/35454

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