An ensemble is a set of learned models that make decisions collectively. Although an ensemble is usually more accurate than a single learner, existing ensemble methods often tend ...
Ensemble pruning is concerned with the reduction of the size of an ensemble prior to its combination. Its purpose is to reduce the space and time complexity of the ensemble and/or ...
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Classification fusion combines multiple classifications of data into a single classification solution of greater accuracy. Feature extraction aims to reduce the computational cost ...
Behrouz Minaei-Bidgoli, Gerd Kortemeyer, William F...
Previously we proposed a scheme to generate fuzzy rule-based multiclassification systems by means of bagging, mutual information-based feature selection, and a multicriteria geneti...
Krzysztof Trawinski, Arnaud Quirin, Oscar Cord&oac...