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Feature Reduction for Neural Network Based Text Categorization
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Savio L.Y. Lam and
Dik Lun Lee
View Paper (PDF)
Return to Session 4A: Data Analysis and Mining
In a text categorization model
using an artificial neural network as the text classifier, scalability is poor
if the neural network is trained using the raw feature space since textural
data has a very high-dimension feature space.
We proposed and compared four
dimensionality reduction techniques to reduce the feature space into an input
space of much lower dimension for the neural network classifier. To test the
effectiveness of the proposed model, experiments were conducted using a subset
of the Reuters-22173 test collection for text categorization.
The results
showed that the proposed model was able to achieve high categorization effectiveness
as measured by precision and recall. Among the four dimensionality reduction
techniques proposed, Principal Component Analysis was found to be the most
effective in reducing the dimensionality of the feature space.
Copyright(C) 2000 ACM
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