A unified, coherent treatment of current classifier ensemble methods, from fundamentals of
pattern recognition to ensemble feature selection, now in its second edition The art and …

Multiple classifier fusion may generate more accurate classification than each of the
constituent classifiers. Fusion is often based on fixed combination rules like the product and …

We study the characteristics of learning with ensembles. Solving exactly the simple model of
an ensemble of linear students, we find surprisingly rich behaviour. For learning in large …

Neural network-based modeling often involves trying multiple networks with different
architectures and training parameters in order to achieve acceptable model accuracy …

Neural-network ensembles have been shown to be very accurate classification techniques.
Previous work has shown that an effec (cid: 173) tive ensemble should consist of networks …

A neural network NN ensemble is a very successful technique where the outputs of a set of
separately trained NNs are combined to form one unified prediction. An effective ensemble …

We suggest two simple ways to use a genetic algorithm (GA) to design a multiple-classifier
system. The first GA version selects disjoint feature subsets to be used by the individual …

A novel method integrating rough sets (RS) theory and an artificial neural network (ANN)
based on data-fusion technique is presented to forecast an air-conditioning load. Data …

In classifier combination, it is believed that diverse ensembles have a better potential for
improvement on the accuracy than non-diverse ensembles. We put this hypothesis to a test …

Neural network ensemble techniques have been shown to be very accurate classification
techniques. However, in some real-life applications a number of classifiers required to …