Comparing Dimensionality Reduction Techniques

William Nick; Joseph Shelton; Gina Bullock; Albert Esterline; Kassahun Asamene

Comparing Dimensionality Reduction Techniques

William Nick, Joseph Shelton, Gina Bullock, Albert Esterline, Kassahun Asamene

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Unknown book
State	Published - 2015

Cite this

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title = "Comparing Dimensionality Reduction Techniques",

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.",

author = "William Nick and Joseph Shelton and Gina Bullock and Albert Esterline and Kassahun Asamene",

note = "Publisher Copyright: {\textcopyright} 2015 IEEE.",

year = "2015",

language = "English",

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TY - GEN

T1 - Comparing Dimensionality Reduction Techniques

AU - Nick, William

AU - Shelton, Joseph

AU - Bullock, Gina

AU - Esterline, Albert

AU - Asamene, Kassahun

PY - 2015

Y1 - 2015

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Unknown book

ER -

Comparing Dimensionality Reduction Techniques

Abstract

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