Comparing Dimensionality Reduction Techniques

Feature selection techniques are investigated to increase the accuracy of classification while reducing the dimensionality of the feature space. Dimensionality reduction techniques investigated include principal component analysis (PCA), recursive feature elimination (RFE), and Genetic and Evolutionary Feature Weighting & Selection (GEFeWS). A support vector machine (SVM) with linear kernel functions was used with all three techniques for consistency. In our experiment, RFE and GEFeWS performed comparably and both resulted in more accurate classifiers than PCA.