Ensuring the robustness and reliability of data-driven knowledge discovery models in production and manufacturing

Shailesh Tripathi, David Muhr, Manuel Brunner, Frank Emmert-Streib, Herbert Jodlbauer, Matthias Dehmer

Publikation: ArbeitspapierVorabpublikation

Abstract

The implementation of robust, stable, and user-centered data analytics and machine learning models is confronted by numerous challenges in production and manufacturing. Therefore, a systematic approach is required to develop, evaluate, and deploy such models. The data-driven knowledge discovery framework provides an orderly partition of the data-mining processes to ensure the practical implementation of data analytics and machine learning models. However, the practical application of robust industry-specific data-driven knowledge discovery models faces multiple data– and model-development–related issues. These issues should be carefully addressed by allowing a flexible, customized, and industry-specific knowledge discovery framework; in our case, this takes the form of the cross-industry standard process for data mining (CRISP-DM). This framework is designed to ensure active cooperation between different phases to adequately address data- and model-related issues. In this paper, we review several extensions of CRISP-DM models and various data-robustness– and model-robustness–related problems in machine learning, which currently lacks proper cooperation between data experts and business experts because of the limitations of data-driven knowledge discovery models.
OriginalspracheEnglisch
Band2007.14791
PublikationsstatusVeröffentlicht - 28 Jul 2020

Publikationsreihe

NamearXiv

Schlagwörter

  • CRISP-DM
  • Data Analytics Applications
  • Industrial production
  • Industry 4.0
  • Machine learning model
  • Robustness
  • Smart manufacturing

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