Knowledge Based Neural Network for Text Classification
Ram Dayal Goyal · 2007 IEEE International Conference on Granular Computing (GRC 2007) · 2007
Automatic text classification has gained huge popularity with the advancement of information technology. Bayesian method has been found highly appropriate for text classification but it suffers from a number of problems. When there is large number of categories, lack of uniformity in training data becomes a big problem. Some nodes may get less training documents, while other may get a very large number. Therefore, some nodes are biased over others. Besides, presence of noise data or outliers also creates problems. Moreover, when documents are very small, just like a line item describing a product, the problem becomes more difficult. In this paper we describe a method that combines naive Bayesian text classification technique and neural networks to handle these problems. We start with a naive Bayesian classifier, which has the linear separating surfaces. We modify the separating surfaces using neural network to find better separating surfaces and hence better classification accuracy over validation data.