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Design and Validation of an Explainable Fuzzy Beer Style Classifier

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Explainable Fuzzy Systems

Part of the book series: Studies in Computational Intelligence ((SCI,volume 970))

Abstract

We describe step by step how to design, implement and validate an interpretable fuzzy rule-based beer style classifier endowed with explanation capability. First, we revise some preliminary work regarding both interpretable fuzzy modeling methodologies and related software. Second, we introduce the use case on beer style classification. Third, we build and validate a fuzzy rule-based classifier with a good interpretability-accuracy trade-off for this use case. Fourth, we endow this classifier with explanation capability through a general linguistic interface that is tuned ad-hoc for the use case under consideration. Fifth, we show how the designed explainable classifier can be refined and exploited with several interoperable software tools. Finally, we compare two kinds of multi-modal (i.e., textual and graphical) explanations: (1) explanations which are inherently natural and fully-meaningful to users, because they are supported by an interpretable fuzzy rule-based classifier which is carefully designed (i.e., it is grounded in common-sense expert knowledge and global semantics); and (2) explanations which are supported by the linguistic approximation of a fuzzy rule-based classifier extracted from data with the focus only on accuracy (thus lacking of linguistic interpretability). The use case is implemented with open source software and all related datasets, tools and scripts are available online for the sake of reproducibility.

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Notes

  1. 1.

    European Commission, Artificial Intelligence for Europe, Brussels, Belgium, “Communication from the Commission to the European Parliament, the European Council, the Council, the European Economic and Social Committee and the Committee of the Regions”, Tech. Rep., 2018, (SWD(2018) 137 final) https://ec.europa.eu/digital-single-market/en/news/communication-artificial-intelligence-europe.

  2. 2.

    https://www.unglobalpulse.org/.

  3. 3.

    ACM US Public Policy Council: Statement on Algorithmic Transparency and Accountability, 2017, https://www.acm.org/binaries/content/assets/public-policy/2017_usacm_statement_algorithms.pdf.

  4. 4.

    https://gitlab.citius.usc.es/jose.alonso/guaje/.

  5. 5.

    GUAJE makes suggestions but the user always makes the final decision. Anyway, unsolved consistency issues can be automatically fixed during the “KB improvement” stage, if the user selects this option.

  6. 6.

    There is some overlapping between the two stages named as “Linguistic rule integration” and “KB improvement”. They may run independently, but if they ran in sequence then tasks (RS.1), (RS.2), (PS.1) and (PS.2) in the “KB improvement” stage would make no sense because the KB should be free of redundant and/or unused elements after fixing consistency issues previously identified during the “Linguistic rule integration” stage.

  7. 7.

    As far as we know, this kind of explanations are only available in GUAJE and ExpliClas (Alonso and Bugarín 2019). The latter is a web service for automatic generation of multi-modal explanations associated to Weka classifiers (Witten et al. 2016).

  8. 8.

    https://scikit-learn.org.

  9. 9.

    An illustrative example is provided in the next section (see Fig. 6.14).

  10. 10.

    We adopt the notation given in Alonso et al. (2019), even if this notation is slightly different from the rest of the book. Namely, \(U\) is an input vector while it refers to the universe of discourse in the rest of the book. In addition, \(T\) is a text generation algorithm while the same symbol refers to the set of linguistic terms in the rest of the book.

  11. 11.

    https://cran.r-project.org/web/packages/rLDCP/index.html.

  12. 12.

    SimpleNLG in Spanish: https://github.com/citiususc/SimpleNLG-ES.

  13. 13.

    SimpleNLG in English: https://github.com/simplenlg/.

  14. 14.

    We recommend to install and run GUAJE (Alonso 2020) in order to appreciate all details. GUAJE lets you zoom in and zoom out of the figures provided as screenshots in the rest of this chapter. Thus, you are not to miss any detail when analyzing the given pictures even if they were printed into black and white.

  15. 15.

    The Beer dataset in arff format is available online at https://gitlab.citius.usc.es/jose.alonso/xai/-/blob/master/BEER3.txt.aux.arff.

  16. 16.

    It is worth noting that GUAJE can run batch scripts from command line with the aim of building models for all the folds at once.

  17. 17.

    The interested reader is kindly invited to revisit Sect. 6.3 and the references there in for further details about how an IFS/GLMP is defined. IFS stands for Interpretable Fuzzy System and GLMP is the acronym of Granular Linguistic Model of a Phenomenon.

  18. 18.

    This Python script is available online along with the rest of files needed to reproduce the use cases illustrated in this chapter (Alonso et al. 2020).

  19. 19.

    The ExpliClas web service:https://demos.citius.usc.es/ExpliClas/.

  20. 20.

    The Waikato Environment for Knowledge Analysis (WEKA): https://www.cs.waikato.ac.nz/ml/weka/.

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Authors and Affiliations

  1. Centro Singular de Investigación en Tecnoloxías Intelixentes (CiTIUS), Universidade de Santiago de Compostela, Santiago de Compostela, Spain

    Jose Maria Alonso Moral

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

    Ciro Castiello & Corrado Mencar

  3. Departamento de Matemática Aplicada a las Tecnologías de la Información y las Comunicaciones, ETSI Informáticos, Universidad Politécnica de Madrid, Madrid, Spain

    Luis Magdalena

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  1. Jose Maria Alonso Moral
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Alonso Moral, J.M., Castiello, C., Magdalena, L., Mencar, C. (2021). Design and Validation of an Explainable Fuzzy Beer Style Classifier. In: Explainable Fuzzy Systems. Studies in Computational Intelligence, vol 970. Springer, Cham. https://doi.org/10.1007/978-3-030-71098-9_6

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