On sequences of different adaptive mechanisms in non-stationary regression problems
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: Proceedings of the International Joint Conference on Neural Networks
Volume: 2015-September
ISBN: 9781479919604
DOI: 10.1109/IJCNN.2015.7280779
Abstract:Existing adaptive predictive methods often use multiple adaptive mechanisms as part of their coping strategy in non-stationary environments. These mechanisms are usually deployed in a prescribed order which does not change. In this work we investigate and provide a comparative analysis of the effects of using a flexible order of adaptive mechanisms' deployment resulting in varying adaptation sequences. As a vehicle for this comparison, we use an adaptive ensemble method for regression in batch learning mode which employs several adaptive mechanisms to react to the changes in data. Using real world data from the process industry we demonstrate that such flexible deployment of available adaptive methods embedded in a cross-validatory framework can benefit the predictive accuracy over time.
Source: Scopus
On Sequences of Different Adaptive Mechanisms In Non-Stationary Regression Problems
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)
ISSN: 2161-4393
Source: Web of Science (Lite)
On Sequences of Different Adaptive Mechanisms in Non-Stationary Regression Problems
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Conference: 2015 International Joint Conference on Neural Networks
Dates: 12-17 July 2015
Source: Manual
On sequences of different adaptive mechanisms in non-stationary regression problems.
Authors: Bakirov, R., Gabrys, B. and Fay, D.
Journal: IJCNN
Pages: 1-8
Publisher: IEEE
ISBN: 978-1-4799-1960-4
https://ieeexplore.ieee.org/xpl/conhome/7256526/proceeding
Source: DBLP