Synergy of clustering multiple back propagation networks
Advances in neural information processing systems 2
Original Contribution: Stacked generalization
Neural Networks
Improving regression estimation: Averaging methods for variance reduction with extensions to general convex measure optimization
Machine Learning
Machine Learning
IEEE Transactions on Pattern Analysis and Machine Intelligence
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Improved boosting algorithms using confidence-rated predictions
COLT' 98 Proceedings of the eleventh annual conference on Computational learning theory
Boosting in the limit: maximizing the margin of learned ensembles
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Boosting classifiers regionally
AAAI '98/IAAI '98 Proceedings of the fifteenth national/tenth conference on Artificial intelligence/Innovative applications of artificial intelligence
Multiclass learning, boosting, and error-correcting codes
COLT '99 Proceedings of the twelfth annual conference on Computational learning theory
Estimating generalization error using out-of-bag estimates
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Feature selection for ensembles
AAAI '99/IAAI '99 Proceedings of the sixteenth national conference on Artificial intelligence and the eleventh Innovative applications of artificial intelligence conference innovative applications of artificial intelligence
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
BoosTexter: A Boosting-based Systemfor Text Categorization
Machine Learning - Special issue on information retrieval
Using a Neural Network to Approximate an Ensemble of Classifiers
Neural Processing Letters
The Role of Occam‘s Razor in Knowledge Discovery
Data Mining and Knowledge Discovery
Learning Algorithms for Keyphrase Extraction
Information Retrieval
Sparse Regression Ensembles in Infinite and Finite Hypothesis Spaces
Machine Learning
Theoretical and Experimental Analysis of a Two-Stage System for Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Constructing Boosting Algorithms from SVMs: An Application to One-Class Classification
IEEE Transactions on Pattern Analysis and Machine Intelligence
ECML '02 Proceedings of the 13th European Conference on Machine Learning
AI '99 Proceedings of the 12th Australian Joint Conference on Artificial Intelligence: Advanced Topics in Artificial Intelligence
Bagging Can Stabilize without Reducing Variance
ICANN '01 Proceedings of the International Conference on Artificial Neural Networks
N-Version Genetic Programming via Fault Masking
EuroGP '02 Proceedings of the 5th European Conference on Genetic Programming
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Theoretical Views of Boosting and Applications
ALT '99 Proceedings of the 10th International Conference on Algorithmic Learning Theory
A Multi-SVM Classification System
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
CAIP '01 Proceedings of the 9th International Conference on Computer Analysis of Images and Patterns
Boosting Density Function Estimators
ECML '02 Proceedings of the 13th European Conference on Machine Learning
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
ICDM '03 Proceedings of the Third IEEE International Conference on Data Mining
IEEE Transactions on Knowledge and Data Engineering
Machine Learning
Intelligent data entry assistant for XML using ensemble learning
Proceedings of the 10th international conference on Intelligent user interfaces
Neural Computation
Neural Networks - 2006 special issue: Earth sciences and environmental applications of computational intelligence
Boosting strategy for classification
Intelligent Data Analysis
A local boosting algorithm for solving classification problems
Computational Statistics & Data Analysis
An efficient modified boosting method for solving classification problems
Journal of Computational and Applied Mathematics
Using an ensemble classifier for machine learning applications
ICCOMP'05 Proceedings of the 9th WSEAS International Conference on Computers
Random classification noise defeats all convex potential boosters
Proceedings of the 25th international conference on Machine learning
Dynamic Weighted Majority: An Ensemble Method for Drifting Concepts
The Journal of Machine Learning Research
Using Boosting to prune Double-Bagging ensembles
Computational Statistics & Data Analysis
Exact bootstrap k-nearest neighbor learners
Machine Learning
A brief introduction to boosting
IJCAI'99 Proceedings of the 16th international joint conference on Artificial intelligence - Volume 2
Ensemble with neural networks for bankruptcy prediction
Expert Systems with Applications: An International Journal
Combining multiple classification or regression models using genetic algorithms
RSCTC'10 Proceedings of the 7th international conference on Rough sets and current trends in computing
Bagging for biclustering: application to microarray data
ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
ICANN'06 Proceedings of the 16th international conference on Artificial Neural Networks - Volume Part II
COLT'05 Proceedings of the 18th annual conference on Learning Theory
IDA'05 Proceedings of the 6th international conference on Advances in Intelligent Data Analysis
Probabilistic ensembles for improved inference in protein-structure determination
Proceedings of the 2nd ACM Conference on Bioinformatics, Computational Biology and Biomedicine
Ensemble methods for biclustering tasks
Pattern Recognition
Improving neural networks classification through chaining
ICANN'12 Proceedings of the 22nd international conference on Artificial Neural Networks and Machine Learning - Volume Part II
Information Sciences: an International Journal
Improving Text Classification Accuracy by Training Label Cleaning
ACM Transactions on Information Systems (TOIS)
Combining multiple predictive models using genetic algorithms
Intelligent Data Analysis - Combined Learning Methods and Mining Complex Data
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An ensemble consists of a set of independently trained classifiers (such as neural networks or decision trees) whose predictions are combined when classifying novel instances. Previous research has shown that an ensemble as a whole is often more accurate than any of the single classifiers in the ensemble. Bagging (Breiman 1996a) and Boosting (Freund & Schapire 1996) are two relatively new but popular methods for producing ensembles. In this paper we evaluate these methods using both neural networks and decision trees as our classification algorithms. Our results clearly show two important facts. The first is that even though Bagging almost always produces a better classifier than any of its individual component classifiers and is relatively impervious to overfitting, it does not generalize any better than a baseline neural-network ensemble method. The second is that Boosting is a powerful technique that can usually produce better ensembles than Bagging; however, it is more susceptible to noise and can quickly overfit a data set.