Representativeness and Uncertainty in Classification Schemes

  • Paul R. Cohen
  • Alvah Davis
  • David Day
  • Michael Greenberg
  • Rick Kjeldsen
  • Susan Lander
  • Cynthia Loiselle


The choice of implication as a representation for empirical associations and for deduction as a model of inference requires a mechanism extraneous to deduction to manage uncertainty associated with inference. Consequently, the interpretation of representations of uncertainty is unclear. Representativeness, or degree of fit, is proposed as an interpretation of degree of belief for classification tasks. The calculation of representativeness depends on the nature of the associations between evidence and conclusions. Patterns of associations are characterized as endorsements of conclusions. We discuss an expert system that uses endorsements to control the search for the most representative conclusion, given evidence.
How to Cite
Cohen, P. R., Davis, A., Day, D., Greenberg, M., Kjeldsen, R., Lander, S., & Loiselle, C. (1985). Representativeness and Uncertainty in Classification Schemes. AI Magazine, 6(3), 136.