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A conceptual MCDA-based framework for machine learning algorithm selection in the early phase of product development

Published online by Cambridge University Press:  16 May 2024

Sebastian Sonntag ,
Erik Pohl ,
Janosch Luttmer ,
Jutta Geldermann  and
Arun Nagarajah
Sebastian Sonntag*
Affiliation:
University of Duisburg-Essen, Germany
Erik Pohl
Affiliation:
University of Duisburg-Essen, Germany
Janosch Luttmer
Affiliation:
University of Duisburg-Essen, Germany
Jutta Geldermann
Affiliation:
University of Duisburg-Essen, Germany
Arun Nagarajah
Affiliation:
University of Duisburg-Essen, Germany
*
sebastian.sonntag@uni-due.de

Abstract

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Despite the potential to enhance efficiency and improve quality, AI methods are not widely adopted in the context of product development due to the need for specialized applications. The necessary identification of a suitable machine learning (ML) algorithm requires expert knowledge, often lacking in companies. Therefore, a concept based on a multi-criteria decision analysis is applied, enabling the identification of a suitable ML algorithm for tasks in the early phase of product development. The application and resulting advantages of the concept are presented through a practical example.

Keywords

artificial intelligence (AI)machine learningproduct developmentdecision makingmulti-criteria decision aiding (MCDA)

Information

Type
Artificial Intelligence and Data-Driven Design
Information
Proceedings of the Design Society , Volume 4: DESIGN 2024 , May 2024 , pp. 2257 - 2266
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
The Author(s), 2024.

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