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

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

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-review

8 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication7th International Conference on Knowledge Management in Organizations : Service and Cloud Computing
Number of pages12
Volume172
PublisherSpringer Publishing Company
Publication date2013
Pages47-58
ISBN (Print)978-3-642-30866-6
ISBN (Electronic)978-3-642-30867-3
DOIs
Publication statusPublished - 2013
Event7th International Conference on Knowledge Management in Organizations - Salamanca, Spain
Duration: 11 Jul 201213 Jul 2012
Conference number: 7th

Conference

Conference7th International Conference on Knowledge Management in Organizations
Number7th
CountrySpain
CitySalamanca
Period11/07/201213/07/2012
SeriesAdvances in Intelligent Systems and Computing
Volume172
ISSN1615-3871

Keywords

  • Ensemble Learning, Classifier, Ensemble Pruning, Diversity

Cite this

Fu, B., Wang, Z., Pan, R., Xu, G., & Dolog, P. (2013). An Integrated Pruning Criterion for Ensemble Learning Based on Classification Accuracy and Diversity. In 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing (Vol. 172, pp. 47-58). Springer Publishing Company. Advances in Intelligent Systems and Computing, Vol.. 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. Vol. 172 Springer Publishing Company, 2013. pp. 47-58 (Advances in Intelligent Systems and Computing, Vol. 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. in 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. vol. 172, Springer Publishing Company, Advances in Intelligent Systems and Computing, vol. 172, pp. 47-58, 7th International Conference on Knowledge Management in Organizations, Salamanca, Spain, 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. Vol. 172 Springer Publishing Company, 2013. p. 47-58 (Advances in Intelligent Systems and Computing, Vol. 172).

Research output: Contribution to book/anthology/report/conference proceedingArticle in proceedingResearchpeer-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. In 7th International Conference on Knowledge Management in Organizations: Service and Cloud Computing. Vol. 172. Springer Publishing Company. 2013. p. 47-58. (Advances in Intelligent Systems and Computing, Vol. 172). https://doi.org/10.1007/978-3-642-30867-3_5