Method of learning implication networks from empirical data: algorithm and Monte-Carlo simulation-based validation

Jiming LIU*, Michel C. Desmarais

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

An algorithmic means for inducing implication networks from empirical data samples are described. The induced network enables efficient inferences about the values of network nodes if certain observations are made. This implication induction method is approximate in nature as probabilistic network requirements are relaxed in the construction of dependence relationships based on statistical testing. The effectiveness and validity of the induction method are examined by conducting Monte Carlo simulations. The values in the implication networks are also predicted by applying a modified version of the Dempster-shafer belief updating scheme.

Original languageEnglish
Pages (from-to)990-1004
Number of pages15
JournalIEEE Transactions on Knowledge and Data Engineering
Volume9
Issue number6
DOIs
Publication statusPublished - Nov 1997

Scopus Subject Areas

  • Information Systems
  • Computer Science Applications
  • Computational Theory and Mathematics

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