Decision Tree Induction Systems: A Bayesian Analysis
  Decision tree induction systems are being used for knowledge acquisition in
noisy domains. This paper develops a subjective Bayesian interpretation of the
task tackled by these systems and the heuristic methods they use. It is argued
that decision tree systems implicitly incorporate a prior belief that the
simpler (in terms of decision tree complexity) of two hypotheses be preferred,
all else being equal, and that they perform a greedy search of the space of
decision rules to find one in which there is strong posterior belief. A number
of improvements to these systems are then suggested.
