Genetic algorithms for feature selection and weighting, a review and study

TitleGenetic algorithms for feature selection and weighting, a review and study
Publication TypeConference Paper
Year of Publication2001
AuthorsHussein, F., N. Kharma, and R. Ward
Conference NameDocument Analysis and Recognition, 2001. Proceedings. 6thInternational Conference on
Pagination1240 -1244
Keywordscharacter recognition, classification accuracy, classification module, feature selection, genetic algorithms, learning (artificial intelligence), pattern classification, pattern recognition applications, probability, search problems, search space, weighting

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


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