Classification Performance Using Principal Component Analysis and Different Value of the Ratio R

  • Jasmina Novakovic “Faculty of Computer Science” Megatrend University Belgrade Serbia, 11000 Belgrade, Bulevar Umetnosti 29
  • Sinisa Rankov Megatrend University Belgrade Bulevar Umetnosti 29

Abstract

A comparison between several classification algorithms with feature extraction on real dataset is presented. Principal Component Analysis (PCA) has been used for feature extraction with different values of the ratio R, evaluated and compared using four different types of classifiers on two real benchmark data sets. Accuracy of the classifiers is influenced by the choice of different values of the ratio R. There is no best value of the ratio R, for different datasets and different classifiers accuracy curves as a function of the number of features used may significantly differ. In our cases feature extraction is especially effective for classification algorithms that do not have any inherent feature selections or feature extraction build in, such as the nearest neighbour methods or some types of neural networks.

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Published
2011-06-01
How to Cite
NOVAKOVIC, Jasmina; RANKOV, Sinisa. Classification Performance Using Principal Component Analysis and Different Value of the Ratio R. INTERNATIONAL JOURNAL OF COMPUTERS COMMUNICATIONS & CONTROL, [S.l.], v. 6, n. 2, p. 317-327, june 2011. ISSN 1841-9844. Available at: <http://univagora.ro/jour/index.php/ijccc/article/view/2180>. Date accessed: 27 sep. 2020. doi: https://doi.org/10.15837/ijccc.2011.2.2180.

Keywords

feature extraction, linear feature extraction methods, principal component analysis, classification algorithms, classification accuracy