Automatic document classification based on probabilistic reasoning: model and performance analysis
Wai Pang Lam, Kon Fan Low · 2002
We develop a new approach to test classification based on automatic feature extraction and probabilistic reasoning. The knowledge representation used to perform such task is known as Bayesian inference networks. A Bayesian network text classifier is automatically constructed from a set of training test documents. We have conducted a series of experiments on two text document corpus, namely the CACM and Reuters, to analyze the performance of our approach, which are described in the paper.