Selection of Graph-Based Features for Character Recognition Using Similarity Based Feature Dependency and Rough Set Theory

  • Sunanda Das
  • Suvra jyoti Choudhury
  • Asit Kumar Das
  • Jaya Sil
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 266)


Recently, large amount of data is populated almost in every field, analysis of which is a challenging task in data mining community. Feature based character recognition is a well-known field of research where numerous features are used without analyzing their importance resulting lengthy recognition process. Feature selection plays an important role in character recognition problem which has not been explored. In the paper, the characters are represented by graphs and features of the graphs form feature vectors. A novel feature selection method has been proposed using the concepts of feature dependency and rough set theory to select only the features which are important for character recognition. Initially, feature dependency is measured based on correlation coefficients and similarity among the features are evaluated using feature dependency based on which the features are ranked. Rough set theory based quick reduct generation algorithm is applied for selecting the important features using feature ranking. The method is applied on character data set as well as on various benchmark data set and the experimental result is compared with well-defined dimension reduction techniques that demonstrates the effectiveness of the method.


Character recognition Feature dependency Similarity measure Feature selection Rough set theory 


  1. 1.
    Dash, M., Liu, H.: Feature selection for classification. Intell. Data Anal. 1(3), 131–156 (1997)CrossRefGoogle Scholar
  2. 2.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning About Data. Kluwer Academic Publishing, Dordrecht (1991)CrossRefMATHGoogle Scholar
  3. 3.
    Polkowski, L.: Rough Sets: Mathematical Foundations. Advances in Soft Computing. Physica Verlag, Heidelberg, Germany. 2002Google Scholar
  4. 4.
    Li, Geng., Semerci, M., Yener, B., Zaki, M.J.: Graph classification via topological and label featuresGoogle Scholar
  5. 5.
  6. 6.
    He, X.C., Yung, N.H.C.: Curvature scale space corner detector with adaptive threshold and dynamic region of support. In: Proceedings of the 17th international conference on pattern recognition, vol. 2, pp. 791–794, August 2004Google Scholar
  7. 7.
    He, X.C., Yung, N.H.C.: Corner detector based on global and local curvature properties. Opt. Eng. 47(5), 057008 (2008)CrossRefGoogle Scholar
  8. 8.
    Yu, L., Liu, H.: Efficient feature selection via analysis of relevance and redundancy. J. Mach. Learn. Res. 5, 1205–1224 (2004)MATHGoogle Scholar
  9. 9.
    Hall, M.A.: Correlation-based feature selection for machine learning. Dissertation, The University of Waikato (1999) Google Scholar
  10. 10.
    Hall, M., Frank, E., Holmes, G., Pfahringer, B., Reutemann, P., Witten, I.H.: The WEKA data mining software: an update. SIGKDD Explor. 11(1), 10–18 (2009)Google Scholar

Copyright information

© Springer India 2014

Authors and Affiliations

  • Sunanda Das
    • 1
  • Suvra jyoti Choudhury
    • 2
  • Asit Kumar Das
    • 3
  • Jaya Sil
    • 3
  1. 1.Neotia Institute of Technology, Management and ScienceSouth 24 ParganaIndia
  2. 2.Department of Purabi Das School of Information TechnologyBengal Engineering and Science UniversityShibpur, HowrahIndia
  3. 3.Department of Computer Science and TechnologyBengal Engineering and Science UniversityShibpur, HowrahIndia

Personalised recommendations