By Topic

An optimized hill climbing algorithm for feature subset selection: evaluation on handwritten character recognition

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$31 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

4 Author(s)
Nunes, C.M. ; Pontificia Universidade Catolica do Parana, Curitiba, Brazil ; Britto, Ad.S., Jr. ; Kaestner, C.A.A. ; Sabourin, R.

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.

Published in:

Frontiers in Handwriting Recognition, 2004. IWFHR-9 2004. Ninth International Workshop on

Date of Conference:

26-29 Oct. 2004