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Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning pp 189–210Cite as

Evaluation of the Developed Approach

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Abstract

The evaluation results derived from the previous application section are presented in a condensed fashion and critically discussed within this section. The critical discussion is roughly structured along the previously presented research hypotheses. Following, the limitations identified during the evaluation and analysis including data pre-processing are highlighted. Within that section the implications of those limitations on the hypotheses and the research results are illustrated.

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Notes

  1. 1.

    Specifications of machine used: Processor: 2.6 GHz dual-core Intel Core i5 processor (Turbo Boost up to 3.1 GHz) with 3 MB shared L3 cache (fourth generation Intel Haswell); Ram: 8 GB of 1600 MHz DDR3; SSD: 512 GB PCIe; Graphics: Intel Iris 1024 MB; OS: OS X 10.9.2.

References

  • Bratko, I., & Suc, D. (2003). Qualitative data mining and its applications. Journal of Computing and Information Technology, 3, 145–150. doi:10.1109/ITI.2003.1225313.

    Article  Google Scholar 

  • Guyon, I., Weston, J., Barnhill, S., & Vapnik, V. (2002). A gene selection method for cancer classification using Support Vector Machines. Machine Learning, 46, 389–422. doi:10.1155/2012/586246.

    Article  MATH  Google Scholar 

  • McCann, M. & Johnston, A. (2008). SECOM data set. UCI Machine Learning Repository. http://archive.ics.uci.edu/ml. Irvine, CA: University of California, School of Information and Computer Science.

  • Zhang, S., Jin, Z., Zhu, X., & Zhang, J. (2009). Missing data analysis: A kernel-based multi-imputation. In M. L. Gavrilova & C. J. K. Tan (Eds.) Transaction on Computer Science III, LNCS 5300 (pp. 122–142). Berlin: Springer.

    Google Scholar 

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Author information

Authors and Affiliations

  1. Department of ICT Applications for Production, BIBA—Bremer Institut für Produktion und Logistik GmbH, Bremen, Germany

    Thorsten Wuest

  2. Department of Production Engineering, University of Bremen, Bremen, Germany

    Thorsten Wuest

Authors
  1. Thorsten Wuest
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Correspondence to Thorsten Wuest .

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© 2015 Springer International Publishing Switzerland

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Wuest, T. (2015). Evaluation of the Developed Approach. In: Identifying Product and Process State Drivers in Manufacturing Systems Using Supervised Machine Learning. Springer Theses. Springer, Cham. https://doi.org/10.1007/978-3-319-17611-6_7

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  • DOI: https://doi.org/10.1007/978-3-319-17611-6_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17610-9

  • Online ISBN: 978-3-319-17611-6

  • eBook Packages: EngineeringEngineering (R0)

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