Welcome to the InfoSci Platform
A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets

A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets

Periasamy Vivekanandan, Raju Nedunchezhian
Copyright: © 2011 |Volume: 2 |Issue: 1 |Pages: 10
ISSN: 1942-3594|EISSN: 1942-3608|EISBN13: 9781613505533|DOI: 10.4018/jaec.2011010104
Cite Article Cite Article

MLA

Vivekanandan, Periasamy, and Raju Nedunchezhian. "A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets." IJAEC vol.2, no.1 2011: pp.49-58. http://doi.org/10.4018/jaec.2011010104

APA

Vivekanandan, P. & Nedunchezhian, R. (2011). A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets. International Journal of Applied Evolutionary Computation (IJAEC), 2(1), 49-58. http://doi.org/10.4018/jaec.2011010104

Chicago

Vivekanandan, Periasamy, and Raju Nedunchezhian. "A Fast Boosting Based Incremental Genetic Algorithm for Mining Classification Rules in Large Datasets," International Journal of Applied Evolutionary Computation (IJAEC) 2, no.1: 49-58. http://doi.org/10.4018/jaec.2011010104

Export Reference

Mendeley
Favorite Full-Issue Download

Abstract

Genetic algorithm is a search technique purely based on natural evolution process. It is widely used by the data mining community for classification rule discovery in complex domains. During the learning process it makes several passes over the data set for determining the accuracy of the potential rules. Due to this characteristic it becomes an extremely I/O intensive slow process. It is particularly difficult to apply GA when the training data set becomes too large and not fully available. An incremental Genetic algorithm based on boosting phenomenon is proposed in this paper which constructs a weak ensemble of classifiers in a fast incremental manner and thus tries to reduce the learning cost considerably.

Request Access

You do not own this content. Please login to recommend this title to your institution's librarian or purchase it from the IGI Global bookstore.