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Interpretability in Machine Learning – Principles and Practice

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Fuzzy Logic and Applications (WILF 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8256))

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Abstract

Theoretical advances in machine learning have been reflected in many research implementations including in safety-critical domains such as medicine. However this has not been reflected in a large number of practical applications used by domain experts. This bottleneck is in a significant part due to lack of interpretability of the non-linear models derived from data. This lecture will review five broad categories of interpretability in machine learning - nomograms, rule induction, fuzzy logic, graphical models & topographic mapping. Links between the different approaches will be made around the common theme of designing interpretability into the structure of machine learning models, then using the armoury of advanced analytical methods to achieve generic non-linear approximation capabilities.

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Author information

Authors and Affiliations

  1. School of Computing and Mathematical Sciences, Liverpool John Moores University, Liverpool, L3 3AF, UK

    P. J. G. Lisboa

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  1. P. J. G. Lisboa
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Editor information

Editors and Affiliations

  1. DIBRIS, University of Genoa, Via Dodecaneso 35, 16146, Genoa, Italy

    Francesco Masulli

  2. Dept. of Informatics, Systems, and Communication, University of Milano Bicocca, Viale Sarca 336, 20126, Milan, Italy

    Gabriella Pasi

  3. Dept. of Information Systems, Iona College, 710 North Ave, 10801, New Rochelle, NY, USA

    Ronald Yager

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Lisboa, P.J.G. (2013). Interpretability in Machine Learning – Principles and Practice . In: Masulli, F., Pasi, G., Yager, R. (eds) Fuzzy Logic and Applications. WILF 2013. Lecture Notes in Computer Science(), vol 8256. Springer, Cham. https://doi.org/10.1007/978-3-319-03200-9_2

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  • DOI: https://doi.org/10.1007/978-3-319-03200-9_2

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-03199-6

  • Online ISBN: 978-3-319-03200-9

  • eBook Packages: Computer ScienceComputer Science (R0)

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