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2004
Springer

Feature Subset Selection Using an Optimized Hill Climbing Algorithm for Handwritten Character Recognition

10 years 1 months ago
Feature Subset Selection Using an Optimized Hill Climbing Algorithm for Handwritten Character Recognition
This paper presents an optimized Hill Climbing algorithm to select a subset of features for handwritten character recognition. The search is conducted taking into account a random mutation strategy and the initial relevance of each feature in the recognition process. The experiments have shown a reduction in the original number of features used in an MLP-based character recognizer from 132 to 77 features (reduction of 42%) without a significant loss in terms of recognition rates, which are 99% for 60,089 samples of digits, and 93% for 11,941 uppercase characters, both handwritten samples from the NIST SD19 database. The proposed method has shown to be an interesting strategy to implement a wrapper approach without the need of complex and expensive hardware architectures.
Carlos M. Nunes, Alceu de Souza Britto Jr., Celso
Added 02 Jul 2010
Updated 02 Jul 2010
Type Conference
Year 2004
Where SSPR
Authors Carlos M. Nunes, Alceu de Souza Britto Jr., Celso A. A. Kaestner, Robert Sabourin
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