Abstract
A new feature selection methodology on the basis of features’ combined class separability power, using the framework of Axiomatic Fuzzy Set (AFS) theory has been proposed here. The AFS theory provides the rules for logic operations needed to interpret the combinations of features from the fuzzy feature set. Based on these combinational rules, class separability power of the combined features is determined and subsequently the most powerful subset of the feature set is selected. The performance of this methodology is evaluated upon for recognition of handwritten numerals of five popular Indic scripts viz. Bangla, Devanagari, Roman, Telugu and Arabic with SVM based classifier using gradient based directional feature set and quad-tree based longest-run feature set separately and compared with six widely used feature selection techniques. From the experimental results, it has been found that the methodology provides higher recognition accuracies with lesser or equal numbers of features selected for each dataset.
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Roy, A., Das, N., Sarkar, R., Basu, S., Kundu, M., Nasipuri, M. (2014). An Axiomatic Fuzzy Set Theory Based Feature Selection Methodology for Handwritten Numeral Recognition. In: Satapathy, S., Avadhani, P., Udgata, S., Lakshminarayana, S. (eds) ICT and Critical Infrastructure: Proceedings of the 48th Annual Convention of Computer Society of India- Vol I. Advances in Intelligent Systems and Computing, vol 248. Springer, Cham. https://doi.org/10.1007/978-3-319-03107-1_16
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DOI: https://doi.org/10.1007/978-3-319-03107-1_16
Publisher Name: Springer, Cham
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