Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Algorithms: design techniques and analysis
Algorithms: design techniques and analysis
Machine Learning
Cost complexity-based pruning of ensemble classifiers
Knowledge and Information Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
Combining Artificial Neural Nets: Ensemble and Modular Multi-Net Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
A decision-theoretic generalization of on-line learning and an application to boosting
EuroCOLT '95 Proceedings of the Second European Conference on Computational Learning Theory
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Getting the Most Out of Ensemble Selection
ICDM '06 Proceedings of the Sixth International Conference on Data Mining
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
The build of n-Bits Binary Coding ICBP Ensemble System
Neurocomputing
A competitive ensemble pruning approach based on cross-validation technique
Knowledge-Based Systems
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Although the Directed Hill Climbing Ensemble Pruning (DHCEP) algorithm has achieved favorable classification performance, it often yields suboptimal solutions to the ensemble pruning problem, due to its limited exploration within the whole solution space, which inspires us with the development of a novel Ensemble Pruning algorithm based on Randomized Greedy Selective Strategy and Ballot (RGSS&B-EP), where randomization technique is introduced into the procedure of greedy ensemble pruning, and the final pruned ensemble is generated by ballot, which are the two major contributions of this paper. Experimental results, including t-tests on the three benchmark classification tasks, verified the validity of the proposed RGSS&B-EP algorithm.