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., Fay, D. and IEEE

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

Preferred by: Rashid Bakirov

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

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

Source: DBLP