| Title: |
Pruning an Ensemble of Classifiers via Reinforcement Learning |
| Author(s): |
I. Partalas, G. Tsoumakas, I. Vlahavas.
|
| Availability: |
Click here to download the PDF (Acrobat Reader) file.
|
| Keywords: |
Ensemble Pruning, Reinforcement Learning.
|
| Appeared in: |
Neurocomputing, Elsevier, 72(7-9), pp. 1900-1909, 2009.
|
| Abstract: |
This paper studies the problem of pruning an ensemble of
classifiers from a Reinforcement Learning perspective. It
contributes a new pruning approach that uses the Q-learning
algorithm in order to approximate an optimal policy of choosing
whether to include or exclude each classifier from the ensemble.
Extensive experimental comparisons of the proposed approach
against state-of-the-art pruning and combination methods show very
promising results. Additionally, we present an extension that
allows the improvement of the solutions returned by the proposed
approach over time, which is very useful in certain
performance-critical domains. |
| See also : |
|
This paper has been cited by the following:
| 1 |
Qiang-Li Zhao, Yan-Huang Jiang, Ming Xu, A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process, Data Mining and Knowledge Discovery, Springer Verlag, 2009. |
| 2 |
Li, K., Han, Y. (2010) Study of selective ensemble learning method and its diversity based on decision tree and neural network, 2010 Chinese Control and Decision Conference, CCDC 2010, art. no. 5498179, pp. 1310-1315.
|
| 3 |
Feng Wang, Cheng Yang, Zhiyi Lin, Yuanxiang Li, Yuan Yuan, (2010) Hybrid sampling on mutual information entropy-based clustering ensembles for optimizations, Neurocomputing 73 (7-9) pp. 1457-1464.
|
| 4 |
Wang, Y., Gao, Y., Shen, R., Yang, F. (2011) Selective ensemble approach for classification of datasets with incomplete values, Advances in Intelligent and Soft Computing, 122, pp. 281-286.
|
| 5 |
Smȩtek, M., Trawiński, B. (2011) Selection of heterogeneous fuzzy model ensembles using self-adaptive genetic algorithms, New Generation Computing, 29 (3), pp. 309-327.
|
| 6 |
Zhao, Q.-L., Jiang, Y.-H., Ming Xu (2011) Incremental learning based on ensemble pruning, Proceedings - 2011 8th International Conference on Fuzzy Systems and Knowledge Discovery, FSKD 2011, 1, art. no. 6019559, pp. 377-381.
|
| 7 |
Han, M., Zhu, X.-R. (2011) A new ensemble algorithm based on oppositional relabeling of artificial data, Dianzi Yu Xinxi Xuebao/Journal of Electronics and Information Technology, 33 (6), pp. 1475-1480.
|
| 8 |
Woloszynski, T., Kurzynski, M. (2011) A probabilistic model of classifier competence for dynamic ensemble selection, Pattern Recognition, Volume 44, Issues 10-11, Semi-Supervised Learning for Visual Content Analysis and Understanding, October-November 2011, Pages 2656-2668, ISSN 0031-3203
|
| 9 |
Xu, Y., Jia, J. (2011) Adaptive Spectral Clustering Ensemble Selection via Resampling and Population-Based Incremental Learning Algorithm, Wuhan University Journal of Natural Sciences, 16 (3), pp. 228-236.
|
| 10 |
Jianhua Jia, Xuan Xiao, Bingxiang Liu, Licheng Jiao (2011) Bagging-based spectral clustering ensemble selection, Pattern Recognition Letters, Volume 32, Issue 10, 15 July 2011, Pages 1456-1467, ISSN 0167-8655
|
| 11 |
Kai Li, Zhibin Liu, Yanxia Han, (2012) Study of Selective Ensemble Learning Methods Based on Support Vector Machine, Physics Procedia, Volume 33, Pages 1518-1525, ISSN 1875-3892, |
| 12 |
Woloszynski, T., Kurzynski, M., Podsiadlo, P., Stachowiak, G.W. A measure of competence based on random classification for dynamic ensemble selection (2012) Information Fusion, 13 (3), pp. 207-213.
|
|
