| Title: |
An Ensemble Uncertainty Aware Measure for Directed Hill Climbing Ensemble Pruning |
| Author(s): |
I. Partalas, G. Tsoumakas, I. Vlahavas.
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| Availability: |
Click here to download the PDF (Acrobat Reader) file (23 pages).
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| Keywords: |
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| Appeared in: |
Machine Learning, Springer, 2010.
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| Abstract: |
This paper proposes a new measure for ensemble pruning via directed hill climbing, dubbed Uncertainty Weighted Accuracy (UWA), which takes into account the uncertainty of the decision of the current ensemble. Empirical results on 30 data sets show that using the proposed measure to prune a heterogeneous ensemble leads to significantly better accuracy results compared to state-of-the-art measures and other baseline methods, while keeping only a small fraction of the original models. Besides the evaluation measure, the paper also studies two other parameters of directed hill climbing ensemble pruning methods, the search direction and the evaluation dataset, with interesting conclusions on appropriate values. |
| See also : |
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This paper has been cited by the following:
| 1 |
Sun, Q., Pfahringer, B. (2011) Bagging ensemble selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7106 LNAI, pp. 251-260.
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| 2 |
Guo, H., Zhi, W., Han, X., Fan, M. (2011) A new metric for greedy ensemble pruning, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7003 LNAI (PART 2), pp. 631-639.
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| 3 |
Elghazel, H., Aussem, A., Perraud, F. (2011) Trading-off diversity and accuracy for optimal ensemble tree selection in random forests, Studies in Computational Intelligence, 373, pp. 169-179.
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| 4 |
Guo, H., Fan, M. (2011) Ensemble Pruning via Base-Classifier Replacement, Proc. 12th International Conference, WAIM 2011, Wuhan, China, September 14-16, 2011, pp. 505-516
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| 5 |
Jafari,S.A.;Mashohor,S.;Ramli,A.R.;Marhaban,M.H. (2012) Expert Pruning Based on Genetic Algorithm in Regression Problems, Proceedings Intelligent Information and Database Systems, pp. 79-88 |
| 6 |
Re,M.;Valentini,G. (2012) Ensemble methods: a review, In: Advances in Machine Learning and Data Mining for Astronomy, Chapman and Hall Data Mining and Knowledge Discovery Series, Chap. 26, pp. 563-594, 2012. |
| 7 |
Zhi, W., Guo, H., Fan, M. (2012) Energy-based metric for ensemble selection, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 7235 LNCS, pp. 306-317.
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