Ensemble pruning via base-classifier replacement
WAIM'11 Proceedings of the 12th international conference on Web-age information management
A new metric for greedy ensemble pruning
AICI'11 Proceedings of the Third international conference on Artificial intelligence and computational intelligence - Volume Part II
AI'11 Proceedings of the 24th international conference on Advances in Artificial Intelligence
Expert pruning based on genetic algorithm in regression problems
ACIIDS'12 Proceedings of the 4th Asian conference on Intelligent Information and Database Systems - Volume Part III
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
Hi-index | 0.00 |
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.