Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing

• URL: http://arxiv.org/abs/2007.14791v1
• Date: Tue, 28 Jul 2020 14:21:14 GMT
• Title: Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
• Authors: Shailesh Tripathi, David Muhr, Brunner Manuel, Frank Emmert-Streib, Herbert Jodlbauer, and Matthias Dehmer
• Abstract summary: Cross-industry standard process for data mining (CRISP-DM) designed to address data- and model-related issues. This paper reviews several extensions of CRISP-DM models and various data-robustness-- and model-robustness-related problems in machine learning.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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