A-optimality orthogonal forward regression algorithm using branch and bound

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), pp. 1961-1967. (doi:10.1109/TNN.2008.2003251). (PMID:19000965).


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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

Item Type: Article
Digital Object Identifier (DOI): doi:10.1109/TNN.2008.2003251
Organisations: Southampton Wireless Group
ePrint ID: 266894
Date :
Date Event
November 2008Published
Date Deposited: 10 Nov 2008 11:09
Last Modified: 17 Apr 2017 18:56
Further Information:Google Scholar
URI: http://eprints.soton.ac.uk/id/eprint/266894

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