  We revisit logistic regression and its nonlinear extensions, including
multilayer feedforward neural networks, by showing that these classifiers can
be viewed as converting input or higher-level features into Dempster-Shafer
mass functions and aggregating them by Dempster's rule of combination. The
probabilistic outputs of these classifiers are the normalized plausibilities
corresponding to the underlying combined mass function. This mass function is
more informative than the output probability distribution. In particular, it
makes it possible to distinguish between lack of evidence (when none of the
features provides discriminant information) from conflicting evidence (when
different features support different classes). This expressivity of mass
functions allows us to gain insight into the role played by each input feature
in logistic regression, and to interpret hidden unit outputs in multilayer
neural networks. It also makes it possible to use alternative decision rules,
such as interval dominance, which select a set of classes when the available
evidence does not unambiguously point to a single class, thus trading reduced
error rate for higher imprecision.
