Abstract
This paper proposes a novel method called FLGP to
construct a classifier device of capability in feature
selection and feature extraction. FLGP is developed
with layered genetic programming that is a kind of the
multiple-population genetic programming. Populations
advance to an optimal discriminant function to divide
data into two classes. Two methods of feature selection
are proposed. New features extracted by certain layer
are used to be the training set of next layer's
populations. Experiments on several well-known datasets
are made to demonstrate performance of FLGP.
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