Machine Learning
Pattern Recognition with Fuzzy Objective Function Algorithms
Pattern Recognition with Fuzzy Objective Function Algorithms
Machine Learning
Expert Systems with Applications: An International Journal
Input Decimated Ensemble based on Neighborhood Preserving Embedding for spectrogram classification
Expert Systems with Applications: An International Journal
Creating ensembles of classifiers via fuzzy clustering and deflection
Fuzzy Sets and Systems
Reduced Reward-punishment editing for building ensembles of classifiers
Expert Systems with Applications: An International Journal
Non-uniform layered clustering for ensemble classifier generation and optimality
ICONIP'10 Proceedings of the 17th international conference on Neural information processing: theory and algorithms - Volume Part I
Predicting shellfish farm closures with class balancing methods
AI'12 Proceedings of the 25th Australasian joint conference on Advances in Artificial Intelligence
Effect of ensemble classifier composition on offline cursive character recognition
Information Processing and Management: an International Journal
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In this work, a new method for the creation of classifier ensembles is introduced. The patterns are partitioned into clusters to group together similar patterns, a training set is built using the patterns that belong to a cluster. Each of the new sets is used to train a classifier. We show that the approach here presented, called FuzzyBagging, obtains performance better than Bagging.