Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis

Michael J. Shaw, James A. Gentry, Selwyn Piramuthu

Research output: Contribution to journalArticlepeer-review

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.

Original languageEnglish (US)
Pages (from-to)147-165
Number of pages19
JournalComputer Science in Economics and Management
Volume3
Issue number2
DOIs
StatePublished - Jun 1 1990

Keywords

  • Inductive learning
  • artificial intelligence
  • commercial loan evaluation
  • decision support systems

ASJC Scopus subject areas

  • Economics, Econometrics and Finance (miscellaneous)

Fingerprint Dive into the research topics of 'Inductive Learning Methods for Knowledge-Based Decision Support: A Comparative Analysis'. Together they form a unique fingerprint.

Cite this