Automated Induction of Rule‐based Neural Networks from Databases

Abstract

This paper describes our approach to the problem of automated knowledge acquisition from large databases of examples using an information-theoretic approach. Our previous research has resulted in practical algorithms (lTRULE) for the automatic induction of rules from large example databases. Utilizing these algorithms, the raw data can be transformed into a set of human readable IF THEN rules, thus giving insight into the knowledge hidden within the data. These rules can then be automatically loaded into an expert system shell. Alternatively, they can be used to build a new type of parallel inference system-a rule-based neural network. This process enables a prototype expert system to be automatically generated and up and running in a matter of minutes, compared with months using a manual knowledge-acquisition approach. The resulting expert system can then be used as a sophisticated search and analysis tool to query the original database capable of reasoning with uncertain and incomplete data.

Neural network-based decision class analysis for building topological-level influence diagram

“…On the other hand, some studies on constructing influence diagrams (or belief networks) from database can be found in the literature (Herskovits & Cooper, 1991;Cooper & Herskovits, 1992), and a general method to address this topic is presented by Goodman and Smyth (1993). Note that these methods handle the belief networks especially in the functional and numerical level rather than the topological level.…”