Open AccessRepresentativeness and Uncertainty in ClassificationSystems
Author(s) -
Cohen Paul,
Davis Alvah,
Day David,
Greenberg Michael,
Kjeldsen Rick,
Lander Susan,
Loiselle Cynthia
Publication year - 1985
Publication title -
ai magazine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.597
H-Index - 79
eISSN - 2371-9621
pISSN - 0738-4602
DOI - 10.1609/aimag.v6i3.495
Subject(s) - representativeness heuristic , inference , interpretation (philosophy) , representation (politics) , computer science , artificial intelligence , data mining , machine learning , information retrieval , mathematics , statistics , political science , politics , law , programming language
The choice of implication as a representation for empirical associations and for deduction as a mode 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.