Title: On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms,
Abstract: We demonstrate that, for a range of state-of-the-art machine learning
algorithms, the differences in generalisation performance obtained using
default parameter settings and using parameters tuned via cross-validation can
be similar in magnitude to the differences in performance observed between
state-of-the-art and uncompetitive learning systems. This means that fair and
rigorous evaluation of new learning algorithms requires performance comparison
against benchmark methods with best-practice model selection procedures, rather
than using default parameter settings. We investigate the sensitivity of three
key machine learning algorithms (support vector machine, random forest and
rotation forest) to their default parameter settings, and provide guidance on
determining sensible default parameter values for implementations of these
algorithms. We also conduct an experimental comparison of these three
algorithms on 121 classification problems and find that, perhaps surprisingly,
rotation forest is significantly more accurate on average than both random
forest and a support vector machine.