On Sequences of Different Adaptive Mechanisms In Non-Stationary Regression Problems

This source preferred by Rashid Bakirov and Damien Fay

Authors: Bakirov, R., Gabrys, B. and Fay, D.

Start date: 12 July 2015

This data was imported from DBLP:

Authors: Bakirov, R., Gabrys, B. and Fay, D.

http://ieeexplore.ieee.org/xpl/mostRecentIssue.jsp?punumber=7256526

Journal: IJCNN

Pages: 1-8

Publisher: IEEE

ISBN: 978-1-4799-1960-4

DOI: 10.1109/IJCNN.2015.7280779

This data was imported from Scopus:

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

© 2015 IEEE. 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.

This data was imported from Web of Science (Lite):

Authors: Bakirov, R., Gabrys, B., Fay, D. and IEEE

Journal: 2015 INTERNATIONAL JOINT CONFERENCE ON NEURAL NETWORKS (IJCNN)

ISSN: 2161-4393

The data on this page was last updated at 04:47 on December 17, 2017.