• Corpus ID: 6586250

Utility Elicitation as a Classification Problem

  title={Utility Elicitation as a Classification Problem},
  author={Urszula Chajewska and Lise Getoor and Joseph Norman and Yuval Shahar},
We investigate the application of classification techniques to utility elicitation. In a decision problem, two sets of parameters must generally be elicited: the probabilities and the utilities. While the prior and conditional probabilities in the model do not change from user to user, the utility models do. Thus it is necessary to elicit a utility model separately for each new user. Elicitation is long and tedious, particularly if the outcome space is large and not decomposable. There are two… 

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  • Computer Science, Economics
    Proceedings. Symposium on Computer Applications in Medical Care
  • 1991
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  • Economics
    IEEE Trans. Syst. Sci. Cybern.
  • 1968
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