Niklas Lavesson MSE-2003:18, pp. 45. Inst. för programvaruteknik och datavetenskap/Dept. of Software Engineering and Computer Science, 2003.
Much research has been done in the fields of
classifier performance evaluation and optimization.
This work summarizes this research and tries to answer
the question if algorithm parameter tuning has more
impact on performance than the choice of algorithm. An
alternative way of evaluation; a measure function is also
demonstrated. This type of evaluation is compared with
one of the most accepted methods; the cross-validation
test. Experiments, described in this work, show that
parameter tuning often has more impact on performance
than the actual choice of algorithm and that the measure
function could be a complement or an alternative to the
standard cross-validation tests.