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
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
EuroCOLT '99 Proceedings of the 4th European Conference on Computational Learning Theory
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Performance Degradation in Boosting
MCS '01 Proceedings of the Second International Workshop on Multiple Classifier Systems
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
AdaBoost with SVM-based component classifiers
Engineering Applications of Artificial Intelligence
Decision Fusion on Boosting Ensembles
ANNPR '08 Proceedings of the 3rd IAPR workshop on Artificial Neural Networks in Pattern Recognition
New Results on Combination Methods for Boosting Ensembles
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Using ARTMAP-Based Ensemble Systems Designed by Three Variants of Boosting
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part I
Improving adaptive boosting with a relaxed equation to update the sampling distribution
IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
Averaged conservative boosting: introducing a new method to build ensembles of neural networks
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
CIARP'07 Proceedings of the Congress on pattern recognition 12th Iberoamerican conference on Progress in pattern recognition, image analysis and applications
Information theoretic combination of pattern classifiers
Pattern Recognition
Ensembles of ARTMAP-based neural networks: an experimental study
Applied Intelligence
Improving adaptive boosting with k-cross-fold validation
ICIC'06 Proceedings of the 2006 international conference on Intelligent Computing - Volume Part I
Combining MF networks: a comparison among statistical methods and stacked generalization
ANNPR'06 Proceedings of the Second international conference on Artificial Neural Networks in Pattern Recognition
ICONIP'06 Proceedings of the 13 international conference on Neural Information Processing - Volume Part I
Introducing reordering algorithms to classic well-known ensembles to improve their performance
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
Improving boosting methods by generating specific training and validation sets
ICONIP'11 Proceedings of the 18th international conference on Neural Information Processing - Volume Part II
| Hi-index | 0.02 |
We lookat three variants of the boosting algorithm called here Aggressive Boosting, Conservative Boosting and Inverse Boosting. We associate the diversity measure Q with the accuracy during the progressive development of the ensembles, in the hope of being able to detect the point of "paralysis" of the training, if any. Three data sets are used: the artificial Cone-Torus data and the UCI Pima Indian Diabetes data and the Phoneme data. We run each of the three Boosting variants with two base classifier models: the quadratic classifier and a multi-layer perceptron (MLP) neural network. The three variants show different behavior, favoring in most cases the Conservative Boosting