C4.5: programs for machine learning
C4.5: programs for machine learning
Machine Learning
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Focused Ensemble Selection: A Diversity-Based Method for Greedy Ensemble Selection
Proceedings of the 2008 conference on ECAI 2008: 18th European Conference on Artificial Intelligence
Predictive Ensemble Pruning by Expectation Propagation
IEEE Transactions on Knowledge and Data Engineering
Constraint projections for ensemble learning
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Ensemble pruning via individual contribution ordering
Proceedings of the 16th ACM SIGKDD international conference on Knowledge discovery and data mining
Hi-index | 0.00 |
Ensemble pruning is a technique to reduce ensemble size and increase its accuracy by selecting an optimal or suboptimal subset as subensemble for prediction. Many ensemble pruning algorithms via greedy search policy have been recently proposed. The key to the success of these algorithms is to construct an effective metric to supervise the search process. In this paper, we contribute a new metric called DBM for greedy ensemble pruning. This metric is related not only to the diversity of base classifiers, but also to the prediction details of current ensemble. Our experiments show that, compared with greedy ensemble pruning algorithms based on other advanced metrics, DBM based algorithm induces ensembles with much better generalization ability.