International Conference on Mining Intelligence and Knowledge Exploration

Mining Intelligence and Knowledge Exploration pp 12-21 | Cite as

Class Specific Feature Selection Using Simulated Annealing

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9468)

Abstract

This paper proposes a method of identifying features which are important for each class. This entails selecting the features specifically for each class. This is carried out by using the simulated annealing technique. The algorithm is run separately for each class resulting in the feature subset for that class. A test pattern is classified by running a classifier for each class and combining the result. The 1NN classifier is the classification algorithm used. Results have been reported on eight benchmark datasets from the UCI repository. The selected features, besides giving good classification accuracy, gives an idea of the important features for each class.

References

  1. 1.
    Mackin, P.D., Roy, A., Mukhopadhyay, S.: Methods for pattern selection, class-specific feature selection and classification for automated learning. Neural Netw. (2013). doi:10.1016/j.neunet.2012.12.007
  2. 2.
    Gilbert, J.E., Soares, C., Williams, P., Dozier, G.: A class-specific ensemble feature selection approach for classification problems. In: ACMSE 2010 (2010)Google Scholar
  3. 3.
    Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1, 131–156 (1997)CrossRefGoogle Scholar
  4. 4.
    Debuse, J.C.W., Rayward-Smith, V.J.: Feature subset selection within a simulated annealing data mining algorithm. J. Intell. Inf. Syst. 9, 57–81 (1997)CrossRefGoogle Scholar
  5. 5.
    Francois, D., de Lannoy, G., Verleysen, M.: Class-specific feature selection for one-against-all multiclass svms. In: ESANN 2011 Proceedings, European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, pp. 263–268 (2011)Google Scholar
  6. 6.
    Guyon, I., Eliseeff, A.: An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157–1182 (2003)MATHGoogle Scholar
  7. 7.
    Oh, J.-S.L.I.-S., Moon, B.-R.: Hybrid genetic algorithms for feature selection. IEEE Trans. PAMI 26(11), 1424–1437 (2004)CrossRefGoogle Scholar
  8. 8.
    Lanzi, P.L.: Fast feature selection with genetic algorithms: a filter approach. In: IEEE International Conference on Evolutionary Computation, pp. 537–540 (1997)Google Scholar
  9. 9.
    Lie, Y., Wang, G., Chen, H., Dong, H., Zhu, X., Wang, S.: An improved particle swarm optimization for feature selection. J. Bionic Eng. 8, 191–200 (2011)CrossRefGoogle Scholar
  10. 10.
    Murty, M.N., Devi, V.S.: Pattern Recognition : An Algorithmic Approach. Undergraduate Topics in Computer Science. Springer, London (2011)CrossRefGoogle Scholar
  11. 11.
    UCI Repository of Machine Learning Databases (1998). http://www.ics.uci.edu/mlearn/MLRepository.html
  12. 12.
    Chen, Y.W., Lin, C.J.: Combining svms with various feature selection strategies. Strat. 324(1), 1–10 (2006)Google Scholar
  13. 13.
    Zhang, H., Sun, G.: Feature selection using tabu search method. Pattern Recog. 35, 701–711 (2002)MATHCrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.Department of Computer Science and AutomationIndian Institute of ScienceBangaloreIndia

Personalised recommendations