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A PATTERN LANGUAGE APPROACH TO IDENTIFY APPROPRIATE MACHINE LEARNING ALGORITHMS IN THE CONTEXT OF PRODUCT DEVELOPMENT

Published online by Cambridge University Press:  19 June 2023

Sebastian Sonntag ,
Janosch Luttmer ,
Robin Pluhnau  and
Arun Nagarajah
Sebastian Sonntag*
Affiliation:
University of Duisburg-Essen
Janosch Luttmer
Affiliation:
University of Duisburg-Essen
Robin Pluhnau
Affiliation:
University of Duisburg-Essen
Arun Nagarajah
Affiliation:
University of Duisburg-Essen
*
Sonntag, Sebastian, Universität Duisburg-Essen, Germany, sebastian.sonntag@uni-due.de

Abstract

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The product development process faces several challenges, such as an increasing and differentiated number of customer requirements, increasing product complexity, and shortened time-to-market. To address these challenges, the implementation of automation approaches in form of machine learning (ML) algorithms appears promising. However, companies lack the implementation of these approaches in their processes, inter alia due to inadequate knowledge and experience in this field. Therefore, the aim of this paper is to develop a structured formulized way of characterising ML algorithms, which can support non-experts in identifying the optimal algorithm to solve a given problem. First, existing approaches covering the determination of appropriate ML algorithms for a given task are examined. Based on this, a pattern language approach is introduced to characterise ML algorithms and problems, allowing matching to be performed to identify the most suitable one for a given task. Due to their broad application, the concept is demonstrated by creating patterns for decision trees and artificial neural networks. A study is conducted to prove that the proposed concept is appropriate to support the ML algorithm selection.

Keywords

Product developmentArtificial intelligenceMachine learningPattern languageDecision making

Information

Type
Article
Information
Proceedings of the Design Society , Volume 3: ICED23 , July 2023 , pp. 365 - 374
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), 2023. Published by Cambridge University Press

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