The Strength of Weak Learnability
Machine Learning
Decision Combination in Multiple Classifier Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
IEEE Transactions on Pattern Analysis and Machine Intelligence
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
Machine Learning
Ensembling neural networks: many could be better than all
Artificial Intelligence
Ensemble Methods in Machine Learning
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Feature combination using boosting
Pattern Recognition Letters
An analysis of diversity measures
Machine Learning
An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Predictive Ensemble Pruning by Expectation Propagation
IEEE Transactions on Knowledge and Data Engineering
Classifier combination based on confidence transformation
Pattern Recognition
Sparse ensembles using weighted combination methods based on linear programming
Pattern Recognition
Multiobjective Neural Network Ensembles Based on Regularized Negative Correlation Learning
IEEE Transactions on Knowledge and Data Engineering
Making use of population information in evolutionary artificialneural networks
IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Diversity regularized ensemble pruning
ECML PKDD'12 Proceedings of the 2012 European conference on Machine Learning and Knowledge Discovery in Databases - Volume Part I
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Classifier ensemble has been intensively studied with the aim of overcoming the limitations of individual classifier components in two prevalent directions, i.e., to diversely generate classifier components, and to sparsely combine multiple classifiers. Currently, most approaches are emphasized only on sparsity or on diversity. In this paper, we investigated classifier ensemble with learning both sparsity and diversity using a heuristic method. We formulated the sparsity and diversity learning problem in a general mathematical framework which is beneficial for learning sparsity and diversity while grouping classifiers. Moreover, we proposed a practical approach based on the genetic algorithm for the optimization process. In order to conveniently evaluate the diversity of component classifiers, we introduced the diversity contribution ability to select proper classifier components and evolve classifier weights. Experimental results on several UCI classification data sets confirm that our approach has a promising sparseness and the generalization performance.