LogitBoost with Trees Applied to the WCCI 2006 Performance Prediction Challenge Datasets

We apply LogitBoost with a tree-based learner to the five WCCI 2006 performance prediction challenge datasets. The number of iterations and the tree size is estimated by 10-fold cross-validation. We add a simple shrinkage strategy to make the algorithm more stable. The results are very promising sin...

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Bibliographic Details
Published inThe 2006 IEEE International Joint Conference on Neural Network Proceedings pp. 1657 - 1660
Main Author Lutz, R.W.
Format Conference Proceeding
LanguageEnglish
Published IEEE 2006
Subjects
Bit error rate
Error analysis
Logistics
Predictive models
Protection
Regression tree analysis
Seminars
Statistics
Terminology
Testing
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Summary:We apply LogitBoost with a tree-based learner to the five WCCI 2006 performance prediction challenge datasets. The number of iterations and the tree size is estimated by 10-fold cross-validation. We add a simple shrinkage strategy to make the algorithm more stable. The results are very promising since we won the challenge.
ISBN:9780780394902
0780394909
ISSN:2161-4393
2161-4407
DOI:10.1109/IJCNN.2006.246633