Selection of Graph-Based Features for Character Recognition Using Similarity Based Feature Dependency and Rough Set Theory
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.
KeywordsCharacter recognition Feature dependency Similarity measure Feature selection Rough set theory
- 3.Polkowski, L.: Rough Sets: Mathematical Foundations. Advances in Soft Computing. Physica Verlag, Heidelberg, Germany. 2002Google Scholar
- 4.Li, Geng., Semerci†, M., Yener, B., Zaki, M.J.: Graph classification via topological and label featuresGoogle Scholar
- 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
- 9.Hall, M.A.: Correlation-based feature selection for machine learning. Dissertation, The University of Waikato (1999) Google Scholar
- 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