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Rule‐Based Expert Systems and Linear Models: An Empirical Comparison of Learning‐By‐Examples Methods *
Author(s) -
Chung HyungMin Michael,
Silver Mark S.
Publication year - 1992
Publication title -
decision sciences
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.238
H-Index - 108
eISSN - 1540-5915
pISSN - 0011-7315
DOI - 10.1111/j.1540-5915.1992.tb00412.x
Subject(s) - computer science , rule induction , machine learning , expert system , task (project management) , artificial intelligence , decision tree , focus (optics) , selection (genetic algorithm) , logistic regression , linear model , data mining , physics , management , optics , economics
Building models of expert decision‐making behavior from examples of experts’ decisions continues to receive considerable research attention. In the 1960's and 70's, linear models derived by statistical methods were studied extensively. More recently, rule‐based expert systems derived by induction algorithms have been the focus of attention. Few studies compare the two approaches. This paper reports on a study that compared linear models derived by logistic regression with rule‐based systems produced by two induction algorithms—ID3 and the genetic algorithm. The techniques performed comparably in modeling the experts at one task, graduate admissions, but differed significantly at a second task, bidder selection.