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A-optimality orthogonal forward regression algorithm using branch and bound

A-optimality orthogonal forward regression algorithm using branch and bound
A-optimality orthogonal forward regression algorithm using branch and bound
In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm
1961-1967
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a
Hong, Xia
e6551bb3-fbc0-4990-935e-43b706d8c679
Chen, Sheng
9310a111-f79a-48b8-98c7-383ca93cbb80
Harris, Chris J.
c4fd3763-7b3f-4db1-9ca3-5501080f797a

Hong, Xia, Chen, Sheng and Harris, Chris J. (2008) A-optimality orthogonal forward regression algorithm using branch and bound. IEEE Transactions on Neural Networks, 19 (11), 1961-1967. (doi:10.1109/TNN.2008.2003251). (PMID:19000965)

Record type: Article

Abstract

In this brief, we propose an orthogonal forward regression (OFR) algorithm based on the principles of the branch and bound (BB) and A-optimality experimental design. At each forward regression step, each candidate from a pool of candidate regressors, referred to as S, is evaluated in turn with three possible decisions: 1) one of these is selected and included into the model; 2) some of these remain in S for evaluation in the next forward regression step; and 3) the rest are permanently eliminated from S . Based on the BB principle in combination with an A-optimality composite cost function for model structure determination, a simple adaptive diagnostics test is proposed to determine the decision boundary between 2) and 3). As such the proposed algorithm can significantly reduce the computational cost in the A-optimality OFR algorithm. Numerical examples are used to demonstrate the effectiveness of the proposed algorithm

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Published date: November 2008
Organisations: Southampton Wireless Group

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Local EPrints ID: 266894
URI: http://eprints.soton.ac.uk/id/eprint/266894
PURE UUID: 338bac0b-6664-4029-a07b-a43a12380958

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Date deposited: 10 Nov 2008 11:09
Last modified: 14 Mar 2024 08:38

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Contributors

Author: Xia Hong
Author: Sheng Chen
Author: Chris J. Harris

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