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Proceedings Paper

Ant colony optimization with selective evaluation for feature selection in character recognition
Author(s): Il-Seok Oh; Jin-Seon Lee
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Paper Abstract

This paper analyzes the size characteristics of character recognition domain with the aim of developing a feature selection algorithm adequate for the domain. Based on the results, we further analyze the timing requirements of three popular feature selection algorithms, greedy algorithm, genetic algorithm, and ant colony optimization. For a rigorous timing analysis, we adopt the concept of atomic operation. We propose a novel scheme called selective evaluation to improve convergence of ACO. The scheme cut down the computational load by excluding the evaluation of unnecessary or less promising candidate solutions. The scheme is realizable in ACO due to the valuable information, pheromone trail which helps identify those solutions. Experimental results showed that the ACO with selective evaluation was promising both in timing requirement and recognition performance.

Paper Details

Date Published: 18 January 2010
PDF: 8 pages
Proc. SPIE 7534, Document Recognition and Retrieval XVII, 75340Y (18 January 2010); doi: 10.1117/12.839924
Show Author Affiliations
Il-Seok Oh, Chonbuk National Univ. (Korea, Republic of)
Jin-Seon Lee, Woosuk Univ. (Korea, Republic of)

Published in SPIE Proceedings Vol. 7534:
Document Recognition and Retrieval XVII
Laurence Likforman-Sulem; Gady Agam, Editor(s)

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