An economic approach to the development of inductive expert systems

Recently, there has been growing interest in the use of machine induction to develop expert systems. This approach offers an alternative to the costly and laborious process of manually extracting human knowledge to develop expert systems. In spite of the increasing commercial interest in inductive expert systems, the approaches used seldom attempt to maximize the value of the system. The authors present an algorithm that develops an inductive expert system with the objective of maximizing system value. They compare the performance of their algorithm to that of the popular ID3 algorithm. In their study, the decision trees produced by their algorithm were able to perform classification tasks at lower cost and were at least as accurate as the decision trees produced by ID3. In addition, their algorithm produced much smaller decision trees than those produced by ID3