Inductive Expert System Design: Maximizing System Value

Vijay S. Mookerjee and Brian L. Dos Santos
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Vijay S. Mookerjee: School of Business, University of Washington, Seattle, Washington 98195
Brian L. Dos Santos: College of Business and Public Administration, University of Louisville, Louisville, Kentucky 40292-0001

Information Systems Research, 1993, vol. 4, issue 2, 111-140

Abstract: There is a growing interest in the use of induction to develop a special class of expert systems known as inductive expert systems . Existing approaches to develop inductive expert systems do not attempt to maximize system value and may therefore be of limited use to firms. We present an induction algorithm that seeks to develop inductive expert systems that maximize value. The task of developing an inductive expert system is looked upon as one of developing an optimal sequential information acquisition strategy. Information is acquired to reduce uncertainty only if the benefits gained from acquiring the information exceed its cost. Existing approaches ignore the costs and benefits of acquiring information. We compare the systems developed by our algorithm with those developed by the popular ID3 algorithm. In addition, we present results from an extensive set of experiments that indicate that our algorithm will result in more valuable systems than the ID3 algorithm and the ID3 algorithm with pessimistic pruning.

Keywords: inductive expert systems; information costs and benefits; economic expert system design; economics of machine learning (search for similar items in EconPapers)
Date: 1993
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