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

arXiv: https://arxiv.org/abs/2007.14791 · HTML: https://mlsanity.com/paper/2007.14791/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Jul 2020.

## Built on by (most cited first, 4 of 4)

- [Intelligent Computing: The Latest Advances, Challenges and Future](https://mlsanity.com/paper/2211.11281/) (2022)
- [Data Issues in Industrial AI System: A Meta-Review and Research Strategy](https://mlsanity.com/paper/2406.15784/) (2024)
- [On The Reliability Of Machine Learning Applications In Manufacturing Environments](https://mlsanity.com/paper/2112.06986/) (2021)
- [An enhanced simulation-based multi-objective optimization approach with knowledge discovery for reconfigurable manufacturing systems](https://mlsanity.com/paper/2212.00581/) (2022)

## Abstract (arXiv)

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.