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Application of SVM to Identify Relevant State Drivers

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

Abstract

In this section, the previously derived hypotheses are evaluated by developing and analyzing three scenarios. The section is structured as follows: at first the scenarios are briefly introduced (for more detail refer to Annex Sect. A.2). The following two subsections focus on the application of the previously introduced research plan on the three scenarios. However, it has to be noted that the scenarios were not evaluated following the presented sequence during the analysis phase. The presented sequence (scenarios I–III) does not resemble the timely sequence of evaluation of the different scenarios. Therefore, it is possible that the background of and justification for some of the methods, tools and applications are explained in later sections even so they are applied beforehand. In such cases, reference is given to the more detailed explanation in later sections. The Chap.  7 presents and discusses the evaluation results and illustrates the limitations of the approach.

Keywords

Feature Selection Classification Performance Minority Class Feature Ranking Target Threshold 
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.

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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

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