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

Content Provider	IEEE Xplore Digital Library
Author	Nunes, C.M. Britto Jr., Ad.S. Kaestner, C.A.A. Sabourin, R.
Copyright Year	2004
Description	Author affiliation: Pontificia Universidade Catolica do Parana, Curitiba, Brazil (Nunes, C.M.; Britto, Ad.S., Jr.; Kaestner, C.A.A.)
Abstract	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.
Sponsorship	Hitachi, Japan IBM, USA Fujitsu Lab., Japan NEC, Japan Toshiba, Japan Microsoft, USA Int. Assoc. for Pattern Recognition
Starting Page	365
Ending Page	370
File Size	132845
Page Count	6
File Format	PDF
ISBN	0769521878
ISSN	15505235
DOI	10.1109/IWFHR.2004.18
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2004-10-26
Publisher Place	Japan
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Character recognition Handwriting recognition Genetic mutations NIST Spatial databases Hardware Feature extraction Costs Filters Information filtering
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
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