A pattern classification approach boosted with genetic algorithms

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Date

2007

Author

Yalabık, İsmet

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Ensemble learning is a multiple-classier machine learning approach which combines, produces collections and ensembles statistical classiers to build up more accurate classier than the individual classiers. Bagging, boosting and voting methods are the basic examples of ensemble learning. In this thesis, a novel boosting technique targeting to solve partial problems of AdaBoost, a well-known boosting algorithm, is proposed. The proposed systems nd an elegant way of boosting a bunch of classiers successively to form a better classier than each ensembled classier. AdaBoost algorithm employs a greedy search over hypothesis space to nd a good suboptimal solution. On the other hand, this work proposes an evolutionary search with genetic algorithms instead of greedy search. Empirical results show that classication with boosted evolutionary computing outperforms AdaBoost in equivalent experimental environments.

Subject Keywords

Electronic Computers., Computer Science.

URI

http://etd.lib.metu.edu.tr/upload/3/12608432/index.pdf
https://hdl.handle.net/11511/16710

Collections

Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

İ. Yalabık, “A pattern classification approach boosted with genetic algorithms,” M.S. - Master of Science, Middle East Technical University, 2007.