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Improving Explanatory Power of Machine Learning in the Symbolic Data Analysis Framework

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Progress in Artificial Intelligence and Pattern Recognition (IWAIPR 2018)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 11047))

Abstract

Many nice machine learning methods are black box producing very efficient rules but hard to be understandable by the users. The aim of this paper is to help user by tools allowing a better comprehension of these rules. These tools are based on characteristic properties of the original variables in order to remain in the natural language of the user. They are based on three principles, first on local models fitting at best clusters to be found, second on a symbolic description of these clusters and their Symbolic Data Analysis, third on characteristic criterion increasing the explanatory power of the rules by an adaptive process filtering explanatory sub populations.

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Authors and Affiliations

  1. CEREMADE, Paris-Dauphine University, Paris, France

    E. Diday

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  1. E. Diday
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Correspondence to E. Diday .

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Editors and Affiliations

  1. Universidad de las Ciencias Informáticas, Havana, Cuba

    Yanio Hernández Heredia

  2. Universidad de las Ciencias Informáticas, Havana, Cuba

    Vladimir Milián Núñez

  3. Universidad de las Ciencias Informáticas, Havana, Cuba

    José Ruiz Shulcloper

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Diday, E. (2018). Improving Explanatory Power of Machine Learning in the Symbolic Data Analysis Framework. In: Hernández Heredia, Y., Milián Núñez, V., Ruiz Shulcloper, J. (eds) Progress in Artificial Intelligence and Pattern Recognition. IWAIPR 2018. Lecture Notes in Computer Science(), vol 11047. Springer, Cham. https://doi.org/10.1007/978-3-030-01132-1_1

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  • DOI: https://doi.org/10.1007/978-3-030-01132-1_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-01131-4

  • Online ISBN: 978-3-030-01132-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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