Abstract
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.
| Original language | English |
|---|---|
| Title of host publication | Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet |
| Pages | 338-343 |
| Number of pages | 6 |
| Volume | 1 |
| Publication status | Published - 2005 |
| Externally published | Yes |
| Event | 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 Duration: 2005 Nov 28 → 2005 Nov 30 |
Other
| Other | 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 |
|---|---|
| City | Vienna |
| Period | 05/11/28 → 05/11/30 |
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ASJC Scopus subject areas
- Engineering(all)
Cite this
Integration of bagging and boosting with a new reweighting technique. / Yasumura, Yoshiaki; Kitani, Naho; Uehara, Kuniaki.
Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet. Vol. 1 2005. p. 338-343 1631289.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
}
TY - GEN
T1 - Integration of bagging and boosting with a new reweighting technique
AU - Yasumura, Yoshiaki
AU - Kitani, Naho
AU - Uehara, Kuniaki
PY - 2005
Y1 - 2005
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=33847204682&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=33847204682&partnerID=8YFLogxK
M3 - Conference contribution
SN - 0769525040
SN - 9780769525044
VL - 1
SP - 338
EP - 343
BT - Proceedings - International Conference on Computational Intelligence for Modelling, Control and Automation, CIMCA 2005 and International Conference on Intelligent Agents, Web Technologies and Internet
ER -