Optimization Approach for Feature Selection and Classification with Support Vector Machine

  • S. Chidambaram
  • K. G. Srinivasagan
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 410)


The support vector machine (SVM) is a most popular tool to resolve the issues related to classification. It prepares a classifier by resolving an optimization problem to make a decision which instances of the training data set are support vectors. Feature selection is also important for selecting the optimum features. Data mining performance gets reduced by Irrelevant and redundant features. Feature selection used to choose a small quantity of related attributes to achieve good classification routine than applying all the attributes. Two major purposes are improving the classification functionalities and reducing the number of features. Moreover, the existing subset selection algorithms consider the work as a particular purpose issue. Selecting attributes are made out by the combination of attribute evaluator and search method using the WEKA Machine Learning Tool. In the proposed work, the SVM classification algorithm is applied by the classifier subset evaluator to automatically separate the standard information set.


Data mining Kernel methods Support vector machine Classification 


  1. 1.
    Huang, C.-L., Chen, M.-C., Wang, C.-J.: Credit scoring with a data mining approach based on support vector machines. Expert Syst. Appl. 33, 847–856 (2007)CrossRefGoogle Scholar
  2. 2.
    Yang, Z., Su, X.: Customer behavior clustering using SVM. Int. Conf. Med. Phys. Biomed. Eng. Elsevier Physics Proc. 33, 1489–1496 (2012)Google Scholar
  3. 3.
    Nematzadeh Balagatabi, Z.: Comparison of decision tree and SVM methods in classification of researcher’s cognitive styles in academic environment. Int. J. Autom. Artif. Intell. 1(1) (2013). ISSN:2320–4001Google Scholar
  4. 4.
    Qi, Z., Tian, Y., Shi, Y.: Structural twin support vector machine for classification. Knowl.-Based Syst. 43, 74–81 (2013). doi: 10.1016/j.knosys.2013.01.008 CrossRefGoogle Scholar
  5. 5.
    Maldonado, S., Weber, R., Basak, J.: Simultaneous feature selection and classification using Kernel-penalized SVM for feature selection. Inform. Sci. 181(1), 115–128 (2011). doi: 10.1016/j.ins.2010.08.047 CrossRefGoogle Scholar
  6. 6.
    Song, L., Smola, A., Gretton, A., Bedo, J., Borgwardt, K.: Feature selection via dependence maximization. J. Mach. Learn. Res. 13(1), 1393–1434 (2012)MathSciNetzbMATHGoogle Scholar
  7. 7.
    Yu, H., Kim, J., Kim, Y., Hwang, S., Lee, Y.H.: An efficient method for learning nonlinear ranking SVM functions. Inform. Sci. 209, 37–48 (2012). doi: 10.1016/j.ins.2012.03.022 MathSciNetCrossRefGoogle Scholar
  8. 8.
    Maldonado, S., Lopez, J.: Imbalanced data classification using second-order cone programming support vector machines. Pattern Recogn. 47(5), 2070–2079 (2014). doi: 10.1016/j.patcog.2013.11.021 CrossRefGoogle Scholar
  9. 9.
    Carrizosa, E., Martín-Barragán, B., Romero-Morales, D.: Detecting relevant variables and interactions in supervised classification. Eur. J. Oper. Res. 213(16), 260–269 (2014). doi: 10.1016/j.ejor.2010.03.020 MathSciNetGoogle Scholar
  10. 10.
    Hassan, R., Othman, R.M., Saad, P., Kasim, S.: A compact hybrid feature vector for an accurate secondary structure prediction. Inform. Sci. 181, 5267–5277 (2011). doi: 10.1016/j.ins.2011.07.019 CrossRefGoogle Scholar
  11. 11.
    Patel, K., Vala, J., Pandya, J.: Comparison of various classification algorithms on iris datasets using WEKA. Int. J. Adv. Eng. Res. Dev. (IJAERD) 1(1) (2014). ISSN:2348–4470Google Scholar
  12. 12.
    Tian, Y., Shi, Y., Liu, X.: Recent advances on support vector machines research. Technol. Econ. Dev. Econ. 18(1), 5–33 (2012)CrossRefGoogle Scholar
  13. 13.
    Victo Sudha George, G., Cyril Raj, V.: Review on feature selection techniques and the impact of SVM for cancer classification using gene expression profile. Int. J. Comput. Sci. Eng. Surv. 2(3), 16–27 (2011)CrossRefGoogle Scholar
  14. 14.
    Qi, Z.Q., Tian, Y.J., Shi, Y.: Robust twin support vector machine for pattern classification. Pattern Recogn. 305–316 (2013)Google Scholar

Copyright information

© Springer India 2016

Authors and Affiliations

  1. 1.Department of ITNational Engineering CollegeKovilpattiIndia
  2. 2.Department of CSENational Engineering CollegeKovilpattiIndia

Personalised recommendations