Title | Genetic algorithms for feature selection and weighting, a review and study |
Publication Type | Conference Paper |
Year of Publication | 2001 |
Authors | Hussein, F., N. Kharma, and R. Ward |
Conference Name | Document Analysis and Recognition, 2001. Proceedings. 6thInternational Conference on |
Pagination | 1240 -1244 |
Keywords | character recognition, classification accuracy, classification module, feature selection, genetic algorithms, learning (artificial intelligence), pattern classification, pattern recognition applications, probability, search problems, search space, weighting |
Abstract | Our aim is: a) to present a comprehensive survey of previous attempts at using genetic algorithms (GA) for feature selection in pattern recognition applications, with a special focus on character recognition; and b) to report on work that uses GA to optimize the weights of the classification module of a character recognition system. The main purpose of feature selection is to reduce the number of features, by eliminating irrelevant and redundant features, while simultaneously maintaining or enhancing classification accuracy. Many search algorithms have been used for feature selection. Among those, GA have proven to be an effective computational method, especially in situations where the search space is uncharacterized (mathematically), not fully understood, or/and highly dimensional |
URL | http://dx.doi.org/10.1109/ICDAR.2001.953980 |
DOI | 10.1109/ICDAR.2001.953980 |