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Explaining Predictions by Characteristic Rules

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Machine Learning and Knowledge Discovery in Databases (ECML PKDD 2022)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 13713))

Abstract

Characteristic rules have been advocated for their ability to improve interpretability over discriminative rules within the area of rule learning. However, the former type of rule has not yet been used by techniques for explaining predictions. A novel explanation technique, called CEGA (Characteristic Explanatory General Association rules), is proposed, which employs association rule mining to aggregate multiple explanations generated by any standard local explanation technique into a set of characteristic rules. An empirical investigation is presented, in which CEGA is compared to two state-of-the-art methods, Anchors and GLocalX, for producing local and aggregated explanations in the form of discriminative rules. The results suggest that the proposed approach provides a better trade-off between fidelity and complexity compared to the two state-of-the-art approaches; CEGA and Anchors significantly outperform GLocalX with respect to fidelity, while CEGA and GLocalX significantly outperform Anchors with respect to the number of generated rules. The effect of changing the format of the explanations of CEGA to discriminative rules and using LIME and SHAP as local explanation techniques instead of Anchors are also investigated. The results show that the characteristic explanatory rules still compete favorably with rules in the standard discriminative format. The results also indicate that using CEGA in combination with either SHAP or Anchors consistently leads to a higher fidelity compared to using LIME as the local explanation technique.

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Notes

  1. 1.

    All the datasets were obtained from https://www.openml.org except Adult, German credit, and Compas.

  2. 2.

    https://github.com/propublica/compas-analysis.

  3. 3.

    CEGA is available at: https://github.com/amrmalkhatib/CEGA.

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Acknowledgement

This work was partially supported by the Wallenberg AI, Autonomous Systems and Software Program (WASP) funded by the Knut and Alice Wallenberg Foundation. HB was partly funded by the Swedish Foundation for Strategic Research (CDA, grant no. BD15-0006).

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Authors and Affiliations

  1. KTH Royal Institute of Technology, Electrum 229, 164 40, Kista, Stockholm, Sweden

    Amr Alkhatib, Henrik Boström & Michalis Vazirgiannis

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  1. Amr Alkhatib
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  2. Henrik Boström
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  3. Michalis Vazirgiannis
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Corresponding author

Correspondence to Amr Alkhatib .

Editor information

Editors and Affiliations

  1. Grenoble Alpes University, Saint Martin d’Hères, France

    Massih-Reza Amini

  2. INSA Rouen Normandy, Saint Etienne du Rouvray, France

    Stéphane Canu

  3. Ruhr-Universität Bochum, Bochum, Germany

    Asja Fischer

  4. KU Leuven, Leuven, Belgium

    Tias Guns

  5. Central European University, Vienna, Austria

    Petra Kralj Novak

  6. Aristotle University of Thessaloniki, Thessaloniki, Greece

    Grigorios Tsoumakas

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Alkhatib, A., Boström, H., Vazirgiannis, M. (2023). Explaining Predictions by Characteristic Rules. In: Amini, MR., Canu, S., Fischer, A., Guns, T., Kralj Novak, P., Tsoumakas, G. (eds) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2022. Lecture Notes in Computer Science(), vol 13713. Springer, Cham. https://doi.org/10.1007/978-3-031-26387-3_24

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  • DOI: https://doi.org/10.1007/978-3-031-26387-3_24

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  • Online ISBN: 978-3-031-26387-3

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