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Machine Learning Paradigms: Theory and Application pp 45–67Cite as

Greedy Selection of Attributes to Be Discretised

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Part of the book series: Studies in Computational Intelligence ((SCI,volume 801))

Abstract

It is well known that discretisation of datasets in some cases may improve the quality of a decision system. Such effects were observed many times during experiments conducted in stylometry domain when authorship attribution tasks were performed. However, some experiments delivered results worse than expected when all attributes in datasets were discretised. Therefore, the idea to test decision systems where only part of attributes is discretised arose. For the selection of attributes to be discretised the greedy forward and backward sequential selection methods were proposed and deeply investigated. Different supervised and unsupervised discretisation methods were employed. The Naive Bayes classifier was selected as the inducer in the decision system. The relation between the subsequent subsets of attributes being discretised and the performance of the decision system was observed. The research proved that there is the maximum of the measure of system quality in respect to the series of subsets of attributes being discretised, generated during the sequential selection processes. Therefore, the attempts to find the optimal subsets of attributes to be discretised are reasonable.

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Acknowledgements

The research described was performed using WEKA workbench [6] at the Silesian University of Technology, Gliwice, Poland, in the framework of the project BK/RAu2/2018.

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Authors and Affiliations

  1. Institute of Computer Science, Silesian University of Technology, Akademicka 16/317, 44-100, Gliwice, Poland

    Grzegorz Baron

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  1. Grzegorz Baron
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Correspondence to Grzegorz Baron .

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  1. Faculty of Computers and Information, Cairo University, Giza, Egypt

    Aboul Ella Hassanien

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Baron, G. (2019). Greedy Selection of Attributes to Be Discretised. In: Hassanien, A. (eds) Machine Learning Paradigms: Theory and Application. Studies in Computational Intelligence, vol 801. Springer, Cham. https://doi.org/10.1007/978-3-030-02357-7_3

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