Advertisement

Evaluation of the Developed Approach

  • Thorsten WuestEmail author
Chapter
Part of the Springer Theses book series (Springer Theses)

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.

Keywords

Feature Selection Classification Performance Minority Class State Driver Feature Ranking 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. 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.CrossRefGoogle Scholar
  2. 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.CrossRefzbMATHGoogle Scholar
  3. 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.
  4. 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

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of ICT Applications for ProductionBIBA—Bremer Institut für Produktion und Logistik GmbHBremenGermany
  2. 2.Department of Production EngineeringUniversity of BremenBremenGermany

Personalised recommendations