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Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04)
An Optimized Hill Climbing Algorithm for Feature Subset Selection: Evaluation on Handwritten Character Recognition
Kokubunji, Tokyo, Japan
October 26-October 29
ISBN: 0-7695-2187-8
Carlos M. Nunes, Pontif?cia Universidade Cat?lica do Paran?
Alceu de S. Britto Jr., Pontif?cia Universidade Cat?lica do Paran? and Universidade Estadual de Ponta Grossa
Celso A. A. Kaestner, Pontif?cia Universidade Cat?lica do Paran?
Robert Sabourin, ?cole de Technologie Sup?rieure
This paper presents an optimized Hill-Climbing algorithm to select 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. A first set of 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 99.1% for 30,089 digits and 93.0% for 11,941 uppercase characters, both handwritten samples from the NIST SD19 database. Additional experiments have been done by considering some loss in terms of recognition rate during the feature subset selection. A byproduct of these experiments is a cascade classifier based on feature subsets of different sizes, which is used to reduce the complexity of the classification task by 86.54% on the digit recognition experiment. The proposed feature selection method has shown to be an interesting strategy to implement a wrapper approach without the need of complex and expensive hardware architectures.
Citation:
Carlos M. Nunes, Alceu de S. Britto Jr., Celso A. A. Kaestner, Robert Sabourin, "An Optimized Hill Climbing Algorithm for Feature Subset Selection: Evaluation on Handwritten Character Recognition," iwfhr, pp.365-370, Ninth International Workshop on Frontiers in Handwriting Recognition (IWFHR'04), 2004
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