  This paper is dedicated to a cautious learning methodology for predicting
preferences between alternatives characterized by binary attributes (formally,
each alternative is seen as a subset of attributes). By "cautious", we mean
that the model learned to represent the multi-attribute preferences is general
enough to be compatible with any strict weak order on the alternatives, and
that we allow ourselves not to predict some preferences if the data collected
are not compatible with a reliable prediction. A predicted preference will be
considered reliable if all the simplest models (following Occam's razor
principle) explaining the training data agree on it. Predictions are based on
an ordinal dominance relation between alternatives [Fishburn and LaValle,
1996]. The dominance relation relies on an uncertainty set encompassing the
possible values of the parameters of the multi-attribute utility function.
Numerical tests are provided to evaluate the richness and the reliability of
the predictions made.
