Abstract
Due to the increase in digitalization Machine Learning (ML)- algorithms bare high potentials for process optimization in the production quality- domain. Nowadays, ML-algorithms are hardly implemented in the production environment. In this paper, we present a tangible use case in which MLalgorithms are applied for predicting the quality of products in a process chain and present the lessons learned we extracted from the application. In the described project, the process of choosing ML-algorithms was a bottleneck. Therefore we describe a promising approach how a decision making tool can help selecting ML-algorithms problem-specifically.
Chapter PDF
Similar content being viewed by others
References
Michael Driscoll (2011) Building data startups: Fast, big, and focused: Low costs and cloud tools are empowering new data startups. http://radar.oreilly.com/2011/08/building-data-startups.html. Accessed 14 May 2018
Peter Sondergaard (2011) Gartner Says Worldwide Enterprise IT Spending to Reach $2.7 Trillion in 2012. https://www.gartner.com/newsroom/id/1824919. Accessed 14 May 2018
Piatetsky-Shapiro G (2014) What main methodology are you using for your analytics, data mining, or data science projects? https://www.kdnuggets.com/polls/2014/analytics-data-mining-data-science-methodology.html. Accessed14 May 2018
Datong P. Zhou, Qie Hu, Claire J. Tomlin (2017) Quantitative comparison of data-driven and physics-based models for commercial building HVAC systems
Pete Chapman, Julian Clinton, Randy Kerber, Thomas Khabaza, Thomas Reinartz, Colin Shearer and Rüdiger Wirth CRISP-DM: Step-by-step data mining guide
Scikit-learn Developers (2018) Decision Tree Classifier. http://scikitlearn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html. Accessed 14 May 2018
Gregory Piatetsky (2018) Survey regarding data mining platforms: What software you used for Analytics, Data Mining, Data Science, Machine Learning projects in the past 12 months? http://vote.sparklit.com/poll.spark/203792. Accessed 14 May 2018
Gregory Piatetsky (2018) Gainers and Losers in Gartner 2018 Magic Quadrant for Data Science and Machine Learning Platforms. https://www.kdnuggets.com/2018/02/gartner-2018-mq-data-science-machine-learningchanges.html. Accessed 14 May 2018
Gregory Piatetsky (2017) Forrester vs Gartner on Data Science Platforms and Machine Learning Solutions. https://www.kdnuggets.com/2017/04/forrestergartner-data-science-platforms-machine-learning.html. Accessed 14 May 2018
Scikit-learn Developers (2018) Decision Trees. http://scikit-learn.org/stable/modules/tree.html. Accessed 14 May 2018
Dr. Jason Brownlee (2017) What is the Difference Between a Parameter and a Hyperparameter? https://machinelearningmastery.com/difference-between-a-parameter-and-a-hyperparameter/. Accessed 14 May 2018
Rafael G. Mantovani, Tomáš Horváth, Ricardo Cerri, Joaquin Vanschoren, André C.P.L.F. de_Carvalho (2016) Hyperparameter Tuning of a Decision Tree Induction Algorithm. IEEE, Piscataway, NJ
Pawel Matuszyk, Rene Tatua Castillo, Daniel Kottke A Comparative Study on Hyperparameter Optimization for Recommender Systems
Mohamed Bekkar, Dr.Hassiba Kheliouane Djemaa, Dr.Taklit Akrouf Alitouche Evaluation Measures for Models Assessment over Imbalanced Data Sets. 2013
Patrick Koch, Brett Wujek, Oleg Golovidov et al. (2017) Automated Hyperparameter Tuning for Effective Machine Learning
Thusberg J, Olatubosun A, Vihinen M (2011) Performance of mutation pathogenicity prediction methods on missense variants. Hum Mutat 32(4): 358–368.doi: 10.1002/humu.21445
(2018) Jupyter Notebook. https://jupyter.readthedocs.io/en/latest/architecture/how_jupyter_ipython_work.html. Accessed 14 May 2018
Mariscal G, Marbán Ó, Fernández C (2010) A survey of data mining and knowledge discovery process models and methodologies. The Knowledge Engineering Review 25(02): 137–166. doi: 10.1017/S0269888910000032
Azevedo A, Santos MF (2008) KDD, SEMMA and CRISP-DM: a parallel overview July 24-26, 2008. Proceedings. In: Abraham A (ed) IADIS European Conference on Data Mining 2008, Amsterdam, The Netherlands, July 24-26, 2008. Proceedings. IADIS, pp 182–185
Piatetsky-Shapiro G (2017) Top Data Science and Machine Learning Methods Used in 2017. https://www.kdnuggets.com/2017/12/top-data-science-machinelearning-methods.html. Accessed 14 May 2018
pakalra, olprod, OpenLocalizationService (2017) Machine Learning – Cheat Sheet für Algorithmen für Microsoft Azure Machine Learning Studio. https://docs.microsoft.com/de-de/azure/machine-learning/studio/algorithmcheat-sheet. Accessed 14 May 2018
Basili VR, Caldiera G, Rombach HD (1994) The Goal Question Metric Approach. In: Encyclopedia of Software Engineering. Wiley
Pitzer E, Affenzeller M (2012) A Comprehensive Survey on Fitness Landscape Analysis. In: Fodor J, Klempous R, Suárez Araujo CP (eds) Recent Advances in Intelligent Engineering Systems, vol 378. Springer Berlin Heidelberg, Berlin, Heidelberg, pp 161–191
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Open Access This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence and indicate if changes were made.
The images or other third party material in this chapter are included in the chapter's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Copyright information
© 2019 The Author(s)
About this paper
Cite this paper
Krauß, J., Frye, M., Beck, G.T.D., Schmitt, R.H. (2019). Selection and Application of Machine Learning- Algorithms in Production Quality. In: Beyerer, J., Kühnert, C., Niggemann, O. (eds) Machine Learning for Cyber Physical Systems. Technologien für die intelligente Automation, vol 9. Springer Vieweg, Berlin, Heidelberg. https://doi.org/10.1007/978-3-662-58485-9_6
Download citation
DOI: https://doi.org/10.1007/978-3-662-58485-9_6
Published:
Publisher Name: Springer Vieweg, Berlin, Heidelberg
Print ISBN: 978-3-662-58484-2
Online ISBN: 978-3-662-58485-9
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)