Learning of expert systems from data
Peter Cheeseman · 1984
This paper describes a method for extracting information from data to form the knowledge base for a probabilistic expert system. The information the method finds consists of joint probabilities that show significant probabilistic connections between the associated attribute values. These joint probabilities can be combined with information about particular cases to compute particular conditional probabilities as described in [3]. The search procedure and significance test required are presented for different types of data. The significance test requires finding if the minimum message length (in the information theory sense) required to encode the data is reduced if the joint probability being tested is given explicitly. This significance test is derived from Bayes' theorem and is shown to find the hypothesis (i.e. set of significant joint probabilities) with the highest posterior probability given the data.