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Ranking-Based Rule Classifier Optimisation

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Advances in Feature Selection for Data and Pattern Recognition

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 138))

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Abstract

Ranking is a strategy widely used for estimating relevance or importance of available characteristic features. Depending on the applied methodology, variables are assessed individually or as subsets, by some statistics referring to information theory, machine learning algorithms, or specialised procedures that execute systematic search through the feature space. The information about importance of attributes can be used in the pre-processing step of initial data preparation, to remove irrelevant or superfluous elements. It can also be employed in post-processing, for optimisation of already constructed classifiers. The chapter describes research on the latter approach, involving filtering inferred decision rules while exploiting ranking positions and scores of features. The optimised rule classifiers were applied in the domain of stylometric analysis of texts for the task of binary authorship attribution.

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Acknowledgements

In the research there was used WEKA workbench [40]. 4eMka Software exploited for DRSA processing [32] was developed at the Laboratory of Intelligent Decision Support Systems, Poznań, Poland. The research was performed at the Silesian University of Technology, Gliwice, within the project BK/RAu2/2017.

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Akademicka 16, 44-100, Gliwice, Poland

    Urszula Stańczyk

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  1. Urszula Stańczyk
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Correspondence to Urszula Stańczyk .

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Editors and Affiliations

  1. Silesian University of Technology , Gliwice, Poland

    Urszula Stańczyk

  2. University of Silesia in Katowice , Katowice, Poland

    Beata Zielosko

  3. University of Bournemouth , Poole, United Kingdom

    Lakhmi C. Jain

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Stańczyk, U. (2018). Ranking-Based Rule Classifier Optimisation. In: Stańczyk, U., Zielosko, B., Jain, L. (eds) Advances in Feature Selection for Data and Pattern Recognition. Intelligent Systems Reference Library, vol 138. Springer, Cham. https://doi.org/10.1007/978-3-319-67588-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-67588-6_7

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  • Online ISBN: 978-3-319-67588-6

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