An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity

Bin Fu, Zhihai Wang, Rong Pan, Guandong Xu, Peter Dolog

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

8 Citationer (Scopus)

Resumé

Ensemble pruning is an important issue in the field of ensemble learning. Diversity is a key criterion to determine how the pruning process has been done and measure what result has been derived. However, there is few formal definitions of diversity yet. Hence, three important factors that should be further considered while designing a pruning criterion is presented, and then an effective definition of diversity is proposed. The experimental results have validated that the given pruning criterion could single out the subset of classifiers that show better performance in the process of hill-climbing search, compared with other definitions of diversity and other criteria.
OriginalsprogEngelsk
Titel7th International Conference on Knowledge Management in Organizations : Service and Cloud Computing
Antal sider12
Vol/bind172
ForlagSpringer Publishing Company
Publikationsdato2013
Sider47-58
ISBN (Trykt)978-3-642-30866-6
ISBN (Elektronisk)978-3-642-30867-3
DOI
StatusUdgivet - 2013
Begivenhed7th International Conference on Knowledge Management in Organizations - Salamanca, Spanien
Varighed: 11 jul. 201213 jul. 2012
Konferencens nummer: 7th

Konference

Konference7th International Conference on Knowledge Management in Organizations
Nummer7th
LandSpanien
BySalamanca
Periode11/07/201213/07/2012
NavnAdvances in Intelligent Systems and Computing
Vol/bind172
ISSN1615-3871

Citer dette

Fu, B., Wang, Z., Pan, R., Xu, G., & Dolog, P. (2013). An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. I 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing (Bind 172, s. 47-58). Springer Publishing Company. Advances in Intelligent Systems and Computing, Bind. 172 https://doi.org/10.1007/978-3-642-30867-3_5
Fu, Bin ; Wang, Zhihai ; Pan, Rong ; Xu, Guandong ; Dolog, Peter. / An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Bind 172 Springer Publishing Company, 2013. s. 47-58 (Advances in Intelligent Systems and Computing, Bind 172).
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Fu, B, Wang, Z, Pan, R, Xu, G & Dolog, P 2013, An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. i 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. bind 172, Springer Publishing Company, Advances in Intelligent Systems and Computing, bind 172, s. 47-58, 7th International Conference on Knowledge Management in Organizations, Salamanca, Spanien, 11/07/2012. https://doi.org/10.1007/978-3-642-30867-3_5

An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. / Fu, Bin; Wang, Zhihai; Pan, Rong; Xu, Guandong; Dolog, Peter.

7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Bind 172 Springer Publishing Company, 2013. s. 47-58 (Advances in Intelligent Systems and Computing, Bind 172).

Publikation: Bidrag til bog/antologi/rapport/konference proceedingKonferenceartikel i proceedingForskningpeer review

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Fu B, Wang Z, Pan R, Xu G, Dolog P. An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. I 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Bind 172. Springer Publishing Company. 2013. s. 47-58. (Advances in Intelligent Systems and Computing, Bind 172). https://doi.org/10.1007/978-3-642-30867-3_5