Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
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
Boosting regression estimators
Neural Computation
Diversity versus Quality in Classification Ensembles Based on Feature Selection
ECML '00 Proceedings of the 11th European Conference on Machine Learning
The Role of Combining Rules in Bagging and Boosting
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
Classifier Combinations: Implementations and Theoretical Issues
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 1
Neural-Based Learning Classifier Systems
IEEE Transactions on Knowledge and Data Engineering
On diversity and accuracy of homogeneous and heterogeneous ensembles
International Journal of Hybrid Intelligent Systems
CTC: An Alternative to Extract Explanation from Bagging
Current Topics in Artificial Intelligence
Diversity in Combinations of Heterogeneous Classifiers
PAKDD '09 Proceedings of the 13th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining
Classifier quality definition on the basis of the estimation calculation approach
EC'09 Proceedings of the 10th WSEAS international conference on evolutionary computing
Coevolutionary multi-population genetic programming for data classification
Proceedings of the 12th annual conference on Genetic and evolutionary computation
Observations on boosting feature selection
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Designing multiple classifier systems for face recognition
MCS'05 Proceedings of the 6th international conference on Multiple Classifier Systems
Selecting structural base classifiers for graph-based multiple classifier systems
MCS'10 Proceedings of the 9th international conference on Multiple Classifier Systems
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In combining classifiers, it is believed that diverse ensembles perform better than non-diverse ones. In order to test this hypothesis, we study the accuracy and diversity of ensembles obtained in bagging and boosting applied to the nearest mean classifier. In our simulation study we consider two diversity measures: the Q statistic and the disagreement measure. The experiments, carried out on four data sets have shown that both diversity and the accuracy of the ensembles depend on the training sample size. With exception of very small training sample sizes, both bagging and boosting are more useful when ensembles consist of diverse classifiers. However, in boosting the relationship between diversity and the efficiency of ensembles is much stronger than in bagging.