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.
Full text not available from this repository.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: | 06 Sep 2021 07:33 |
| URI: | https://usir.salford.ac.uk/id/eprint/35454 |
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