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Growing and pruning selective ensemble regression for nonlinear and nonstationary systems

Growing and pruning selective ensemble regression for nonlinear and nonstationary systems
Growing and pruning selective ensemble regression for nonlinear and nonstationary systems
For a selective ensemble regression (SER) scheme to be effective in online modeling of fast arriving nonlinear and nonstationary data, it must not only be capable of maintaining a most up to date and diverse base model set but also be able to forget old knowledge no longer relevant. Based on these two important principles, in this paper, we propose a novel growing and pruning SER (GAP-SER) for time-varying nonlinear data. Specifically, during online operation, newly emerging process state is automatically identified and a local linear model is fitted to it. This adaptive growing strategy therefore maintains a most up to date and diverse local model set. The online prediction model is then constructed as a selective ensemble from the local linear model set based on a probability metric. Moreover, a pruning strategy is derived to remove 'unwanted' out of date local linear models in order to achieve low online computational complexity without sacrificing online modeling accuracy. A chaotic time series prediction and two real-world data sets are used to demonstrate the superior online modeling performance of the proposed GAP-SER over a range of benchmark schemes for nonlinear and nonstationary systems, in terms of online prediction accuracy and computational complexity.
2169-3536
73278-73292
Liu, Tong
241436a9-55dc-471a-a259-5bb071dadfe6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
38a3138c-b194-4a68-a0ac-cb27f8e6505d
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Liu, Tong
241436a9-55dc-471a-a259-5bb071dadfe6
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Liang, Shan
38a3138c-b194-4a68-a0ac-cb27f8e6505d
Harris, Christopher
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Liu, Tong, Chen, Sheng, Liang, Shan and Harris, Christopher (2020) Growing and pruning selective ensemble regression for nonlinear and nonstationary systems. IEEE Access, 8 (1), 73278-73292. (doi:10.1109/ACCESS.2020.2987815).

Record type: Article

Abstract

For a selective ensemble regression (SER) scheme to be effective in online modeling of fast arriving nonlinear and nonstationary data, it must not only be capable of maintaining a most up to date and diverse base model set but also be able to forget old knowledge no longer relevant. Based on these two important principles, in this paper, we propose a novel growing and pruning SER (GAP-SER) for time-varying nonlinear data. Specifically, during online operation, newly emerging process state is automatically identified and a local linear model is fitted to it. This adaptive growing strategy therefore maintains a most up to date and diverse local model set. The online prediction model is then constructed as a selective ensemble from the local linear model set based on a probability metric. Moreover, a pruning strategy is derived to remove 'unwanted' out of date local linear models in order to achieve low online computational complexity without sacrificing online modeling accuracy. A chaotic time series prediction and two real-world data sets are used to demonstrate the superior online modeling performance of the proposed GAP-SER over a range of benchmark schemes for nonlinear and nonstationary systems, in terms of online prediction accuracy and computational complexity.

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IEEEaccess2020-8-1 - Version of Record
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Accepted/In Press date: 10 April 2020
e-pub ahead of print date: 14 April 2020
Published date: 30 April 2020

Identifiers

Local EPrints ID: 439808
URI: http://eprints.soton.ac.uk/id/eprint/439808
ISSN: 2169-3536
PURE UUID: 519fddd2-9a02-462b-8f43-b9dc29767890

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Date deposited: 05 May 2020 16:30
Last modified: 06 Oct 2020 19:25

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Contributors

Author: Tong Liu
Author: Sheng Chen
Author: Shan Liang
Author: Christopher Harris

University divisions

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