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A Methodology for Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit String Recognition (2003)
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| Venue: | International Journal of Pattern Recognition and Artificial Intelligence |
| Citations: | 34 - 9 self |
BibTeX
@ARTICLE{Oliveira03amethodology,
author = {L. S. Oliveira and R.Sabourin and F. Bortolozzi and C.Y. Suen},
title = {A Methodology for Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit String Recognition},
journal = {International Journal of Pattern Recognition and Artificial Intelligence},
year = {2003},
volume = {17},
pages = {2003}
}
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Abstract
In this paper a methodology for feature selection for the handwritten digit string recognition is proposed. Its novelty lies in the use of a multi-objective genetic algorithm where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate tness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Some advantages of this approach include the ability to accommodate multiple criteria such as number of features and accuracy of the classier, as well as the capacity to deal with huge databases in order to adequately represent the pattern recognition problem. Comprehensive experiments on the NIST SD19 demonstrate the feasibility of the proposed methodology.
