Abstract
Receiver operating characteristic (ROC) methodology is an important tool in decision making where there are two outcomes. By introducing the notion of partial degree of membership, fuzzy ROC methodology extends classical ROC methodology as fuzzy set theory extends classical two-state set theory. This paper introduced fuzzy ROC methodology and demonstrates its importance in decision making. Computational methods are developed and important concepts are demonstrated with examples from clinical medicine. These concepts include objective derivation of fuzzy functions with linguistics modifiers, enhanced certainty in sequential case learning, fuzzy inferencing using multiple fuzzy variables and associations with artificial intelligence expert systems and neural networks.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>
Keywords
Receiver operating characteristicFuzzy logicArtificial intelligenceFuzzy setMachine learningComputer scienceSet (abstract data type)Fuzzy classificationFuzzy set operationsMembership functionType-2 fuzzy sets and systemsFunction (biology)Fuzzy numberData miningMathematics
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Publication Info
- Year
- 2002
- Type
- article
- Volume
- 22
- Pages
- 694-699
- Citations
- 17
- Access
- Closed
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Cite This
J.M. DeLeo,
Gregory Campbell
(2002).
The fuzzy receiver operating characteristic function and medical decisions with uncertainty.
[1990] Proceedings. First International Symposium on Uncertainty Modeling and Analysis
, 22
, 694-699.
https://doi.org/10.1109/isuma.1990.151339
Identifiers
- DOI
- 10.1109/isuma.1990.151339