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Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis |
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| Authors: | Michael J. Shaw James A. Gentry Selwyn Piramuthu |
| Affiliation: | (1) Department of Business Administration and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61820 Champaign, IL, USA;(2) Department of Finance, University of Illinois at Urbana-Champaign, 61820 Champaign, IL, USA;(3) Department of Business Administration and Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, 61820 Champaign, IL, USA |
| Abstract: | This paper describes the inductive learning methods for generating decision rules in decision support systems. Three similarity-based learning systems are studied based on: (1) the AQ-Star method, (2) the Tree-Induction method, and (3) the Probabilistic Learning method. Loan evaluation examples and empirical data are used as a basis for comparing these inductive learning methods on their algorithmic characteristics and decision support performance. |
| Keywords: | Inductive learning decision support systems artificial intelligence commercial loan evaluation |
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