Selecting and constructing features using grammatical evolution

Authors:
Dimitris Gavrilis;Ioannis G. Tsoulos;Evangelos Dermatas
Affiliations:
Department of Electrical and Computer Engineering, University of Patras, Greece;Department of Computer Science, University of Ioannina, Greece;Department of Electrical and Computer Engineering, University of Patras, Greece
Venue:
Pattern Recognition Letters
Year:
2008

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Abstract

A novel method for feature selection and construction is introduced. The method improves the classification accuracy, utilizing the well-established technique of grammatical evolution by creating non-linear mappings of the original features to artificial ones in order to improve the effectiveness of artificial intelligence tools such as multi-layer perceptron (MLP), Radial-basis-function (RBF) neural networks and nearest neighbor (KNN) classifier. The proposed method has been applied on a series of classification and regression problems and an experimental comparison is carried out against the accuracy obtained on the original features as well as on features created by the PCA method.