Excellence in Research and Innovation for Humanity
%0 Journal Article
%A Asha Rani and Christian Micheloni and  Gian Luca Foresti
%D 2009 
%J  International Journal of Computer, Electrical, Automation, Control and Information Engineering
%B World Academy of Science, Engineering and Technology
%I International Science Index 31, 2009
%T Balancing Neural Trees to Improve Classification Performance
%U http://waset.org/publications/8593
%V 31
%X In this paper, a neural tree (NT) classifier having a
simple perceptron at each node is considered. A new concept for
making a balanced tree is applied in the learning algorithm of the
tree. At each node, if the perceptron classification is not accurate and
unbalanced, then it is replaced by a new perceptron. This separates
the training set in such a way that almost the equal number of patterns
fall into each of the classes. Moreover, each perceptron is trained only
for the classes which are present at respective node and ignore other
classes. Splitting nodes are employed into the neural tree architecture
to divide the training set when the current perceptron node repeats
the same classification of the parent node. A new error function based
on the depth of the tree is introduced to reduce the computational
time for the training of a perceptron. Experiments are performed to
check the efficiency and encouraging results are obtained in terms of
accuracy and computational costs.
%P 1779 - 1783