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On Interpretability and Similarity in Concept-Based Machine Learning

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Analysis of Images, Social Networks and Texts (AIST 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12602))

Abstract

Machine Learning (ML) provides important techniques for classification and predictions. Most of these are black-box models for users and do not provide decision-makers with an explanation. For the sake of transparency or more validity of decisions, the need to develop explainable/interpretable ML-methods is gaining more and more importance. Certain questions need to be addressed:

  • How does an ML procedure derive the class for a particular entity?

  • Why does a particular clustering emerge from a particular unsupervised ML procedure?

  • What can we do if the number of attributes is very large?

  • What are the possible reasons for the mistakes for concrete cases and models?

For binary attributes, Formal Concept Analysis (FCA) offers techniques in terms of intents of formal concepts, and thus provides plausible reasons for model prediction. However, from the interpretable machine learning viewpoint, we still need to provide decision-makers with the importance of individual attributes to the classification of a particular object, which may facilitate explanations by experts in various domains with high-cost errors like medicine or finance.

We discuss how notions from cooperative game theory can be used to assess the contribution of individual attributes in classification and clustering processes in concept-based machine learning. To address the 3rd question, we present some ideas on how to reduce the number of attributes using similarities in large contexts.

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Notes

  1. 1.

    One of the earlier precursors of association rules can be also found in [17] under the name of “almost true implications”.

  2. 2.

    Similarity between concepts is discussed in [9].

  3. 3.

    https://github.com/dimachine/Shap4JSM.

  4. 4.

    \(S\uparrow \) is the up-set of S in the Boolean lattice \((\mathcal {P}\{Ma, Mi, Se, L\},\subseteq )\).

  5. 5.

    https://archive.ics.uci.edu/ml/datasets/zoo.

  6. 6.

    https://github.com/dimachine/ShapStab/.

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Acknowledgements

The study was implemented in the framework of the Basic Research Program at the National Research University Higher School of Economics and funded by the Russian Academic Excellence Project ‘5–100’. The second author was also supported by Russian Science Foundation under grant 17-11-01276 at St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, Russia. The second author would like to thank Fuad Aleskerov, Alexei Zakharov, and Shlomo Weber for the inspirational lectures on Collective Choice and Voting Theory.

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Authors and Affiliations

  1. Bern University of Applied Sciences, Bern, Switzerland

    Léonard Kwuida

  2. National Research University Higher School of Economics, Moscow, Russian Federation

    Dmitry I. Ignatov

  3. St. Petersburg Department of Steklov Mathematical Institute of Russian Academy of Sciences, St. Petersburg, Russia

    Dmitry I. Ignatov

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  1. Léonard Kwuida
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  2. Dmitry I. Ignatov
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Correspondence to Léonard Kwuida .

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Editors and Affiliations

  1. RWTH Aachen University, Aachen, Germany

    Wil M. P. van der Aalst

  2. University of Ljubljana, Ljubljana, Slovenia

    Vladimir Batagelj

  3. National Research University Higher School of Economics, Moscow, Russia

    Dmitry I. Ignatov

  4. Krasovskii Institute of Mathematics and Mechanics, Yekaterinburg, Russia

    Michael Khachay

  5. National Research University Higher School of Economics, St. Petersburg, Russia

    Olessia Koltsova

  6. University of Oslo, Oslo, Norway

    Andrey Kutuzov

  7. National Research University Higher School of Economics, Moscow, Russia

    Sergei O. Kuznetsov

  8. National Research University Higher School of Economics, Moscow, Russia

    Irina A. Lomazova

  9. Moscow State University, Moscow, Russia

    Natalia Loukachevitch

  10. LORIA, Vandœuvre lès Nancy, France

    Amedeo Napoli

  11. Skolkovo Institute of Science and Technology, Moscow, Russia

    Alexander Panchenko

  12. University of Florida, Gainesville, FL, USA

    Panos M. Pardalos

  13. Università Ca' Foscari Venezia, Venice, Italy

    Marcello Pelillo

  14. National Research University Higher School of Economics, Nizhny Novgorod, Russia

    Andrey V. Savchenko

  15. Kazan Federal University, Kazan, Russia

    Elena Tutubalina

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Kwuida, L., Ignatov, D.I. (2021). On Interpretability and Similarity in Concept-Based Machine Learning. In: van der Aalst, W.M.P., et al. Analysis of Images, Social Networks and Texts. AIST 2020. Lecture Notes in Computer Science(), vol 12602. Springer, Cham. https://doi.org/10.1007/978-3-030-72610-2_3

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