Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition

This paper discusses the use of genetic algorithm for feature selection for handwriting recognition. Its novelty lies in the use of a multi-objective genetic algorithms where sensitivity analysis and neural network are employed to allow the use of a representative database to evaluate fitness and the use of a validation database to identify the subsets of selected features that provide a good generalization. Comprehensive experiments on the NIST database confirm the effectiveness of the proposed strategy.

• Type: Conference
• Year: 2002
• Where: ICPR
• Authors: Luiz E. Soares de Oliveira, Robert Sabourin, Flávio Bortolozzi, Ching Y. Suen
• Keywords: Computer Vision, Genetic Algorithm, ICPR 2002, Multi-objective Genetic Algorithms, NIST Database