Integration of bagging and boosting with a new reweighting technique

Yoshiaki Yasumura, Naho Kitani, Kuniaki Uehara

研究成果: Conference contribution

3 被引用数 (Scopus)

抄録

We propose a novel ensemble learning method, IBB (Integration of Boosting and Bagging). This method creates initial classifiers by bagging, and then builds base classifiers by boosting using the previously created classifiers. IBB has two new techniques, a reweighting technique and data adaptation. The reweighting technique increases a weight of a sample which is misclassified by both the ensemble classifier and previously created base classifier. The data adaptation is realized by controlling the number of iteration in boosting. Experimental results using the datasets of UCI machine learning repository show that IBB resulted better accuracy than the other ensemble learning methods on several datasets and on average.

本文言語English
ホスト出版物のタイトルProceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
ページ338-343
ページ数6
出版ステータスPublished - 2005
外部発表はい
イベントInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005 - Vienna, Austria
継続期間: 2005 11月 282005 11月 30

出版物シリーズ

名前Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
1

Conference

ConferenceInternational Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet Commerce, IAWTIC 2005
国/地域Austria
CityVienna
Period05/11/2805/11/30

ASJC Scopus subject areas

  • 工学(全般)

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