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Analysis of Multiple Classifiers Performance for Discretized Data in Authorship Attribution

  • Grzegorz Baron
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 73)

Abstract

In authorship attribution domain single classifiers are often employed in research as elements of decision system. On the other hand, there is intuitive prediction that the use of multiple classifier with fusion of their outcomes may improve the quality of the investigated system. Additionally, discretization can be applied for input data which can be beneficial for the classification accuracy. The paper presents performance analysis of some multiple classifiers basing on the majority voting rule. Ensembles were composed from eight single well known classifiers. Influence of different discretization methods on the quality of the analyzed systems was also investigated.

Keywords

Multiple classifier Ensemble classifier Majority voting Discretization Authorship attribution 

Notes

Acknowledgments

The research described was performed at the Silesian University of Technology, Gliwice, Poland, in the framework of the project BK/RAu2/2017. All experiments were performed using WEKA workbench [9].

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Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Silesian University of TechnologyGliwicePoland

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