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

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) a...

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Published in:Proceedings of the Design Society Vol. 3; pp. 365 - 374
Main Authors: Sonntag, Sebastian, Luttmer, Janosch, Pluhnau, Robin, Nagarajah, Arun
Format: Journal Article Conference Proceeding
Language:English
Published: Cambridge Cambridge University Press 01.07.2023
Subjects:
Algorithms
Machine learning
Product development
ISSN:2732-527X, 2732-527X
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Abstract 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.
AbstractList 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.
Author Pluhnau, Robin
Sonntag, Sebastian
Nagarajah, Arun
Luttmer, Janosch
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Copyright The Author(s), 2023. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution – Non-Commercial – No Derivatives License 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. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Copyright_xml – notice: The Author(s), 2023. Published by Cambridge University Press. This work is licensed under the Creative Commons Attribution – Non-Commercial – No Derivatives License 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. (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
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SubjectTerms Algorithms
Machine learning
Product development
Title A PATTERN LANGUAGE APPROACH TO IDENTIFY APPROPRIATE MACHINE LEARNING ALGORITHMS IN THE CONTEXT OF PRODUCT DEVELOPMENT
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