FuzzyBagging: A novel ensemble of classifiers
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.
classifier design and evaluation machine learning