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2010

Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling

8 years 3 days ago
Particle Swarm Optimization Aided Orthogonal Forward Regression for Unified Data Modeling
We propose a unified data modeling approach that is equally applicable to supervised regression and classification applications, as well as to unsupervised probability density function estimation. A particle swarm optimization (PSO) aided orthogonal forward regression (OFR) algorithm based on leaveone-out (LOO) criteria is developed to construct parsimonious radial basis function (RBF) networks with tunable nodes. Each stage of the construction process determines the center vector and diagonal covariance matrix of one RBF node by minimizing the LOO statistics. For regression applications, the LOO criterion is chosen to be the LOO mean square error, while the LOO misclassification rate is adopted in two-class classification applications. By adopting the Parzen window estimate as the desired response, the unsupervised density estimation problem is transformed into a constrained regression problem. This PSO aided OFR algorithm for tunable-node RBF networks is capable of constructing very ...
Sheng Chen, Xia Hong, Chris J. Harris
Added 22 May 2011
Updated 22 May 2011
Type Journal
Year 2010
Where TEC
Authors Sheng Chen, Xia Hong, Chris J. Harris
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