Learning internal representations by error propagation
Parallel distributed processing: explorations in the microstructure of cognition, vol. 1
Original Contribution: Stacked generalization
Neural Networks
Machine Learning
Error reduction through learning multiple descriptions
Machine Learning
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Mining frequent patterns by pattern-growth: methodology and implications
ACM SIGKDD Explorations Newsletter - Special issue on “Scalable data mining algorithms”
Ensembling neural networks: many could be better than all
Artificial Intelligence
A Brief Introduction to Boosting
IJCAI '99 Proceedings of the Sixteenth International Joint Conference on Artificial Intelligence
Reducing multiclass to binary: a unifying approach for margin classifiers
The Journal of Machine Learning Research
Ensemble selection from libraries of models
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Using boosting to prune bagging ensembles
Pattern Recognition Letters
Selective fusion of heterogeneous classifiers
Intelligent Data Analysis
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
Guest editors' introduction: special issue of selected papers from ECML PKDD 2009
Data Mining and Knowledge Discovery
Guest editors' introduction: Special Issue from ECML PKDD 2009
Machine Learning
A Fast Ensemble Pruning Algorithm Based on Pattern Mining Process
ECML PKDD '09 Proceedings of the European Conference on Machine Learning and Knowledge Discovery in Databases: Part I
Ensemble approaches for regression: A survey
ACM Computing Surveys (CSUR)
An effective ensemble pruning algorithm based on frequent patterns
Knowledge-Based Systems
Hi-index | 0.00 |
Ensemble pruning deals with the reduction of base classifiers prior to combination in order to improve generalization and prediction efficiency. Existing ensemble pruning algorithms require much pruning time. This paper presents a fast pruning approach: pattern mining based ensemble pruning (PMEP). In this algorithm, the prediction results of all base classifiers are organized as a transaction database, and FP-Tree structure is used to compact the prediction results. Then a greedy pattern mining method is explored to find the ensemble of size k. After obtaining the ensembles of all possible sizes, the one with the best accuracy is outputted. Compared with Bagging, GASEN, and Forward Selection, experimental results show that PMEP achieves the best prediction accuracy and keeps the size of the final ensemble small, more importantly, its pruning time is much less than other ensemble pruning algorithms.