Skip to main content

Part of the book series: Springer Theses ((Springer Theses))

  • 1167 Accesses

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

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 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Thorsten Wuest .

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this chapter

Cite this chapter

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

Download citation

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

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-17610-9

  • Online ISBN: 978-3-319-17611-6

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics