Title :
Comparing dimensionality reduction techniques
Author :
Nick, William ; Shelton, Joseph ; Bullock, Gina ; Esterline, Albert ; Asamene, Kassahun
Author_Institution :
Dept. of Comput. Sci., North Carolina A&T State Univ., Greensboro, NC, USA
Abstract :
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.
Keywords :
data reduction; feature selection; genetic algorithms; pattern classification; principal component analysis; support vector machines; GEFeWS; PCA; dimensionality reduction techniques; feature selection techniques; genetic and evolutionary feature weighting & selection; linear kernel functions; principal component analysis; recursive feature elimination; support vector machine; Accuracy; Evolutionary computation; Frequency modulation; Genetics; Kernel; Principal component analysis; Support vector machines; Dimensionality reduction; Genetic and evolutionary computation; Machine learning;
Conference_Titel :
SoutheastCon 2015
Conference_Location :
Fort Lauderdale, FL
DOI :
10.1109/SECON.2015.7132997