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The Data Dimensionality Reduction in the Classification Process Through Greedy Backward Feature Elimination

Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 659)

Abstract

The article presents the author’s algorithm of dimensionality reduction of used data set, realized through Greedy Backward Feature Elimination. Results of the dimensionality reduction are verified in the process of classification for 2 selected data sets. These data sets contain the data for the realization of the multiclass classification. The article presents not only a description of the algorithm but also an example and the results of classification, carried out by selected classifier before and after the process of dimensionality reduction. At the end of article, a summary and the possibility of further work are provided.

Keywords

Dimensionality reduction Feature selection Algorithm Classification Multiclass Kappa WEKA UCI URBAN DIGITS 

Notes

Acknowledgements

This work was partly supported by BKM16/RAU2/507 and BK-219/RAU2/2016 grants from the Institute of Informatics, Silesian University of Technology, Poland.

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland

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