This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the …

Background A survey of neural network ensemble is given. Neural network ensemble can
significantly improve the generalization ability of learning systems through training a finite …

Neural network ensemble is a learning paradigm where several neural networks are jointly
used to solve a problem. In this paper, the relationship between the generalization ability of …

Ensemble learning is an imperative study in the domain of machine learning. Over the
previous years, ensemble learning has drawn considerable attention in the field of artificial …

In order for a neural network ensemble to generalise properly, two factors are considered
vital. One is the diversity and the other is the accuracy of the networks that comprise the …

Neural network ensemble is a learning paradigm where many neural networks are jointly
used to solve a problem. In this paper, the relationship between the ensemble and its …

Combining the outputs of several neural networks into an aggregate output often gives
improved accuracy over any individual output. The set of networks is known as an ensemble …

The ensemble learning approach has been increasingly used in data mining for improving
performance. However, the gain on the learning performance appears varying considerably …

Several means for improving the performance and training of neural networks for
classification are proposed. Crossvalidation is used as a tool for optimizing network …

Presents a constructive algorithm for training cooperative neural-network ensembles
(CNNEs). CNNE combines ensemble architecture design with cooperative training for …