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International Workshop on New Frontiers in Mining Complex Patterns

NFMCP 2016: New Frontiers in Mining Complex Patterns pp 39–53Cite as

Bayesian Confirmation Measures in Rule-Based Classification

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Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10312))

Abstract

With the rapid growth of available data, learning models are also gaining in sizes. As a result, end-users are often faced with classification results that are hard to understand. This problem also involves rule-based classifiers, which usually concentrate on predictive accuracy and produce too many rules for a human expert to interpret. In this paper, we tackle the problem of pruning rule classifiers while retaining their descriptive properties. For this purpose, we analyze the use of confirmation measures as representatives of interestingness measures designed to select rules with desirable descriptive properties. To perform the analysis, we put forward the CM-CAR algorithm, which uses interestingness measures during rule pruning. Experiments involving 20 datasets show that out of 12 analyzed confirmation measures \(c_1\), F, and Z are best for general-purpose rule pruning and sorting. An additional analysis comparing results on balanced/imbalanced and binary/multi-class problems highlights also N, S, and \(c_3\) as measures for sorting rules on binary imbalanced datasets. The obtained results can be used to devise new classifiers that optimize confirmation measures during model training.

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Notes

  1. 1.

    Sources available at: http://www.cs.put.poznan.pl/dbrzezinski/software.php.

  2. 2.

    Supplement: http://www.cs.put.poznan.pl/dbrzezinski/software/CMCAR.html.

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Acknowledgements

This work was supported by the National Science Centre grant DEC-2013/11/B/ST6/00963. D. Brzezinski acknowledges the support of an FNP START scholarship and Institute of Computing Science Statutory Fund.

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Authors and Affiliations

  1. Institute of Computing Science, Poznan University of Technology, ul. Piotrowo 2, 60–965, Poznan, Poland

    Dariusz Brzezinski, Zbigniew Grudziński & Izabela Szczęch

Authors
  1. Dariusz Brzezinski
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  2. Zbigniew Grudziński
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  3. Izabela Szczęch
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Corresponding author

Correspondence to Dariusz Brzezinski .

Editor information

Editors and Affiliations

  1. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Annalisa Appice

  2. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Michelangelo Ceci

  3. Università degli Studi di Bari Aldo Moro, Bari, Italy

    Corrado Loglisci

  4. ICAR-CNR, Rende, Italy

    Elio Masciari

  5. University of North Carolina, Charlotte, North Carolina, USA

    Zbigniew W. Raś

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Brzezinski, D., Grudziński, Z., Szczęch, I. (2017). Bayesian Confirmation Measures in Rule-Based Classification. In: Appice, A., Ceci, M., Loglisci, C., Masciari, E., Raś, Z. (eds) New Frontiers in Mining Complex Patterns. NFMCP 2016. Lecture Notes in Computer Science(), vol 10312. Springer, Cham. https://doi.org/10.1007/978-3-319-61461-8_3

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  • DOI: https://doi.org/10.1007/978-3-319-61461-8_3

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-61460-1

  • Online ISBN: 978-3-319-61461-8

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