An Evolutionary Feature Selection Technique Using Polynomial Neural Network
In this paper we propose a novel approach for feature subset
selection by the Polynomial Neural Network (PNN) using
Genetic Algorithm (GA). A randomly selected subset of
features of a dataset is passed to the PNN as input. The
classification accuracy of PNN is taken as the fitness function
of GA. In the conventional PNN approaches, published in
literature so far, the processing by PNN takes large
computation time due to the expansion of the whole network at
different levels. In the proposed scheme, less number of
features selected stochastically using the GA, prevents PNN to
grow at very early stages which reduces the computation cost
as well as time. The proposed scheme is simulated on six benchmark databases and classification accuracies obtained
using proposed PNN classifiers are compared with those
obtained using three other existing approaches. It is observed
that the classification accuracies using proposed scheme are
quite satisfactory compared to existing three schemes. The
strength of proposed scheme is justified in two ways: (i) its
high classification accuracy with much less computational cost
in the presence of reduced number of features and (ii) much
less execution time taken by it as compared to other schemes.
Keywords: Polynomial Neural Net, Genetic Algorithm, Feature Selection, Pattern Classification
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