Further Experimental Evidence against the Utility of Occam's Razor
  This paper presents new experimental evidence against the utility of Occam's
razor. A~systematic procedure is presented for post-processing decision trees
produced by C4.5. This procedure was derived by rejecting Occam's razor and
instead attending to the assumption that similar objects are likely to belong
to the same class. It increases a decision tree's complexity without altering
the performance of that tree on the training data from which it is inferred.
The resulting more complex decision trees are demonstrated to have, on average,
for a variety of common learning tasks, higher predictive accuracy than the
less complex original decision trees. This result raises considerable doubt
about the utility of Occam's razor as it is commonly applied in modern machine
learning.
