Belief Learning in Certainty Factor Model and its Application to Text Categorization

This paper describes a method of belief learning in certainty factor model and applied to a task of text categorization. Our method uses a multiplicative update algorithm to perform the belief learning of rules and predicts by a certainty (plausible) inference mechanism. The key difference between our method and Sleepingexperts algorithms is that we use the rule’s combination functions instead of the weighted combination of the predictions. When applied to a text categorization task, this method can easily integrates user defined IF-THEN rules due to being compatible with expert system’s rules combination framework. The initial experiments show that the performance of this method is comparable to Sleepingexperts method. Moreover, it has better time and space efficiency than Sleeping-experts method.