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
The Random Subspace Method for Constructing Decision Forests
IEEE Transactions on Pattern Analysis and Machine Intelligence
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
On the Boosting Pruning Problem
ECML '00 Proceedings of the 11th European Conference on Machine Learning
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Pruning and dynamic scheduling of cost-sensitive ensembles
Eighteenth national conference on 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
Pruning in ordered bagging ensembles
ICML '06 Proceedings of the 23rd international conference on Machine learning
Rotation Forest: A New Classifier Ensemble Method
IEEE Transactions on Pattern Analysis and Machine Intelligence
Data Mining: Practical Machine Learning Tools and Techniques, Second Edition (Morgan Kaufmann Series in Data Management Systems)
Statistical Comparisons of Classifiers over Multiple Data Sets
The Journal of Machine Learning Research
Ensemble Pruning Via Semi-definite Programming
The Journal of Machine Learning Research
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
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
Energy-Based metric for ensemble selection
APWeb'12 Proceedings of the 14th Asia-Pacific international conference on Web Technologies and Applications
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Ensemble pruning is a technique to increase ensemble accuracy and reduce its size by choosing an optimal or suboptimal subset of ensemble members to form subensembles for prediction. A number of greedy ensemble pruning methods that are based on greedy search policy have recently been proposed. In this paper, we contribute a new greedy ensemble pruning method, called EPR, based on replacement policy. Unlike traditional pruning methods, EPR searches for the optimal or suboptimal subensemble with predefined size by iteratively replacing the least important classifier in it with current classifier. Especially, replacement would not occur if the current classifier was the least important one. Also, we adopt diversity measure [1] to theoretically analyze the properties of EPR, based on which a new metric is proposed to guide EPR's search process. We evaluate the performance of EPR by comparing it with other advanced greedy ensemble pruning methods and obtain very promising results.