Genetik algoritma ile desteklenmiş bir örüntü sınıflandırma yaklaşımı.

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Tezin Türü: Yüksek Lisans

Tezin Yürütüldüğü Kurum: Orta Doğu Teknik Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü, Türkiye

Tezin Onay Tarihi: 2007

Tezin Dili: İngilizce

Öğrenci: İsmet Yalabık

Danışman: FATOŞ TUNAY YARMAN VURAL

Özet:

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.