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 An Adaptive Meta-Classifier for Text DocumentsAbstractIn this paper we investigated a way to create a new adaptive metaclassifier
for classifying text documents in order to increase the
classification accuracy. During the first processing phase (preclassification)
the meta-classifier uses a non-adaptive selector. The
role of this selector is to implement a data transformation from a
large space representation of the documents (in our case vectors
having 1309 words) into a much smaller space representation,
based on the input data categories (in our case there are 16
categories). This transposition method is based on the categories in
which the input data might be classified (Reuter’s classification)
and it is using SVM and Bayes type classification algorithms. In the
second phase (classification) we use a feed-forward neural network
based on the back-propagation learning method. We chose a neural
network architecture which contains one hidden layer of units with
sigmoid activation function, where each unit from each layer is
connected with all units of the previous layer. The experimental
results have showed that using this adaptive algorithm,
classification accuracy can be significantly improved. For
Reuters2000 text documents we obtained classification accuracy up
to 99.74%.KeywordsMeta-classification, Back propagation Networks and Text Document Classification To see the electronic version of the paper, pleaseClick Here
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