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Adaptive Learning with Covariate Shift-Detection for Non-Stationary Environments

Raza, Haider, Prasad, G and Li, Yuhua (2014) Adaptive Learning with Covariate Shift-Detection for Non-Stationary Environments. In: 2014 14th UK Workshop on Computational Intelligence (UKCI), Bradford, UK. IEEE. 8 pp. [Conference contribution]

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URL: http://ieeexplore.ieee.org/xpl/articleDetails.jsp?reload=true&arnumber=6930161&sortType%3Dasc_p_Sequence%26filter%3DAND%28p_IS_Number%3A6930143%29

DOI: 10.1109/UKCI.2014.6930161

Abstract

Learning with data-set shift is a major challenge in non-stationary environments wherein the input data distribution may shift over time. Detecting the data-set shift point in the time-series data, where the distribution of time-series shifts its properties, is of utmost interest. Data-set 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 data-set shift-detection using an exponential weighted moving average (EWMA) model based test in a non-stationary environment. The proposed method initiates the adaptation by re-configuring 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 data-sets. Results show that it reacts well to different co-variate shifts.

Item Type:Conference contribution (Lecture)
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Intelligent Systems
Research Institutes and Groups:Computer Science Research Institute > Intelligent Systems Research Centre
Computer Science Research Institute
ID Code:30755
Deposited By: Professor Girijesh Prasad
Deposited On:08 Jan 2015 15:12
Last Modified:08 Jan 2015 15:12

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