Application of SVM to Identify Relevant State Drivers

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


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.


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.


  1. Akbani, R., Kwek, S., & Japkowicz, N. (2004). Applying support vector machines to imbalanced datasets. In: Machine Learning: ECML 2004.Google Scholar
  2. Bhuvaneswari, E., & Dhulipala, V. R. S. (2013). The study and analysis of classification algorithm for animal kingdom dataset. Information Engineering, 2(1), 6–13.Google Scholar
  3. Chang, Y., & Lin, C. (2008). Feature ranking using linear SVM. In JMLR: Workshop and Conference Proceedings 3 (pp. 53–64).Google Scholar
  4. Chawla, N. V. (2010). Data mining for imbalanced datasets: An overview. In Maimon, O. & Rokach, L. (Eds.). Data mining and knowledge discovery handbook (pp. 875–886). Springer. doi: 10.1007/978-0-387-09823-4_45.
  5. Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002). SMOTE: Synthetic minority over-sampling technique. Journal of Intelligence Research, 16, 321–357.zbMATHGoogle Scholar
  6. Choi, J. (2010). A selective sampling method for imbalanced data learning on support vector machines. In AAAI’2000 Workshop on Imbalanced Data Sets. Iowa State University.Google Scholar
  7. De Groot, P. J., Postma, G. J., Melssen, W. J., & Buydens, L. M. C. (1999). Selecting a representative training set for the classification of demolition waste using remote NIR sensing. Analytica Chimica Acta, 392(1999), 67–75.CrossRefGoogle Scholar
  8. Eibe, F., Hall, M. & Holland, K. (2014). Class SVMAttributeEval. WEKA package attribute selection. Retrieved March 27, 2014, from
  9. Evgeniou, T., & Pontil, M. (2001). Support vector machines: Theory and applications. In G. Paliouras, V. Karkaletsis, & C. Spyropoulos (Eds.), ACAI’99, LNAI 2049 (pp. 249–257). Berlin, Heidelberg: Springer.Google Scholar
  10. Farquad, M. A. H., & Bose, I. (2012). Preprocessing unbalanced data using support vector machine. Decision Support Systems, 53(1), 226–233. doi: 10.1016/j.dss.2012.01.016.CrossRefGoogle Scholar
  11. 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
  12. Li, Y., & Shawe-Taylor, J. (2003). The SVM with uneven margins and chinese document categorisation. In 17th Pacific Asia Conference on Language Information and Computation (PACLIC17), October (pp. 216–227).Google Scholar
  13. Li, B., Hu, J., & Hirasawa, K. (2008). Support vector machine classifier with WHM offset for unbalanced data. Journal of Advanced Computational Intelligence and Intelligent Infomatics, 12(1), 94–101.Google Scholar
  14. Provost, F. (2000). Machine learning from imbalanced data sets 101. In AAAI’2000 workshop on imbalanced data sets. Google Scholar
  15. Tang, Y., Zhang, Y.-Q., Chawla, N. V, & Krasser, S. (2009). SVMs modeling for highly imbalanced classification. IEEE Transactions on Systems, Man, and Cybernetics. Part B, Cybernetics: A Publication of the IEEE Systems, Man, and Cybernetics Society, 39(1), 281–288. doi: 10.1109/TSMCB.2008.2002909.
  16. Veropoulos, K., Cristianini, N., & Campbell, C. (1999). The application of support vector machines to medical decision support: A case study. In ECCAI Advanced Course in Artificial Intelligence, Chania, Greece (ACAI99) (pp. 17–21).Google Scholar
  17. Wang, B., & Japkowicz, N. (2010). Boosting support vector machines for imbalanced data sets. Knowledge and Information Systems, 25(1), 1–20.CrossRefGoogle Scholar
  18. Wang, L.-H., Liu, J., Li, Y.-F., & Zhou, H.-B. (2004). Predicting protein secondary structure by a support vector machine based on a new coding scheme. Genome Informatics. International Conference on Genome Informatics, 15(2), 181–190.Google Scholar
  19. Wasikowski, M., & Chen, X. (2010). Combating the small sample class imbalance problem using feature selection. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1388–1400.CrossRefGoogle Scholar
  20. Witten, I. H., Frank, E., & Hall, M. A. (2011). Data mining: Practical machine learning tools and techniques (p. 665). Burlington: Elsevier.Google Scholar
  21. Yu, L., & Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205–1224.zbMATHGoogle Scholar
  22. Yun, Z., Nan, M. A., Da, R., & Bing, A. N. (2011). An effective over-sampling method for imbalanced data sets classification. Chinese Journal of Electronics, 20(3), 2–7.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