Ensuring the Robustness and Reliability of Data-Driven Knowledge Discovery Models in Production and Manufacturing
Tripathi, Shailesh; Muhr, David; Brunner, Manuel; Jodlbauer, Herbert; Dehmer, Matthias; Emmert-Streib, Frank (2021-06-14)
Tripathi, Shailesh
Muhr, David
Brunner, Manuel
Jodlbauer, Herbert
Dehmer, Matthias
Emmert-Streib, Frank
14.06.2021
576892
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:tuni-202111128379
https://urn.fi/URN:NBN:fi:tuni-202111128379
Kuvaus
Peer reviewed
Tiivistelmä
The Cross-Industry Standard Process for Data Mining (CRISP-DM) is a widely accepted framework in production and manufacturing. This data-driven knowledge discovery framework provides an orderly partition of the often complex data mining processes to ensure a 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 need to be carefully addressed by allowing a flexible, customized and industry-specific knowledge discovery framework. For this reason, extensions of CRISP-DM are needed. In this paper, we provide a detailed review of CRISP-DM and summarize extensions of this model into a novel framework we call Generalized Cross-Industry Standard Process for Data Science (GCRISP-DS). This framework is designed to allow dynamic interactions between different phases to adequately address data- and model-related issues for achieving robustness. Furthermore, it emphasizes also the need for a detailed business understanding and the interdependencies with the developed models and data quality for fulfilling higher business objectives. Overall, such a customizable GCRISP-DS framework provides an enhancement for model improvements and reusability by minimizing robustness-issues.
Kokoelmat
- TUNICRIS-julkaisut [16977]