Abstract:
Artificial neural networks (ANNs) are
computing models for information processing
and pattern identification. An ANN is a network
of many simple computing units called neurons
or cells, which are highly interconnected and
organized in layers. Ensemble neural network is
a learning paradigm where several neural
networks are jointly used to solve a problem.
Generalization ability of a neural network can be
significantly improved through ensembling
neural networks, i.e. training several neural
networks and combining their results in some
way. Ensemble neural network is a collection of a
(finite) number of neural networks that are
trained for the same task. Since it behaves
remarkably well and is easy to use, ensemble
neural network is regarded as a promising
methodology that can profit not only experts in
neural computing but also ordinary engineers in
real world applications. This paper presents the
ensemble neural network method trained with
clustering can improve the accuracy of the
classifier than single neural network. The system
is test with three datasets from UCI machine
learning repository and results are presented.
