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Interpretable Machine Learning from Granular Computing Perspective

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Applied Decision-Making

Part of the book series: Studies in Systems, Decision and Control ((SSDC,volume 209))

Abstract

Machine Learning (ML) is a method that aims to learn from data to identify patterns and make predictions. Nowadays ML models have become ubiquitous, there are so many services that people use in their daily life, consequently, those systems affect in very ways to the final users. Recently, there is a special interest on the right of the final user to know why the system generates some output; this field is called Interpretable Machine Learning (IML). Granular Computing (GrC) paradigm is focused in knowledge modeling inspired by human thinking. In this work we conduct a survey of the state of the art in IML and GrC fields to settle the bases of the possible contribution of each other with aims to build more interpretable and accurately ML models.

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Notes

  1. 1.

    https://www.darpa.mil/program/explainable-artificial-intelligence.

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Acknowledgements

This research was partially supported by MyDCI (Maestría y Doctorado en Ciencias e Ingeniería).

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Authors and Affiliations

  1. Universidad Autónoma de Baja California, Calzada Universidad 14418, Tijuana, Baja California, Mexico

    Raúl Navarro-Almanza, Juan R. Castro & Mauricio A. Sanchez

Authors
  1. Raúl Navarro-Almanza
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  2. Juan R. Castro
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  3. Mauricio A. Sanchez
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Corresponding author

Correspondence to Raúl Navarro-Almanza .

Editor information

Editors and Affiliations

  1. Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    Mauricio A. Sanchez

  2. Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    Leocundo Aguilar

  3. Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    Manuel Castañón-Puga

  4. Universidad Autónoma de Baja California, Tijuana, Baja California, Mexico

    Antonio Rodríguez

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Navarro-Almanza, R., Castro, J.R., Sanchez, M.A. (2019). Interpretable Machine Learning from Granular Computing Perspective. In: Sanchez, M., Aguilar, L., Castañón-Puga, M., Rodríguez, A. (eds) Applied Decision-Making. Studies in Systems, Decision and Control, vol 209. Springer, Cham. https://doi.org/10.1007/978-3-030-17985-4_8

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