Improving cross-validation based classifier selection using meta-learning

In this paper we compare classifier selection using cross-validation with meta-learning, using as meta-features both the cross-validation errors and other measures characterizing the data. Through simulation experiments we demonstrate situations where meta-learning offers better classifier selections than ordinary cross-validation. The results provide some evidence to support meta-learning not just as a more time efficient classifier selection technique than cross-validation, but potentially as more accurate. It also provides support for the usefulness of data complexity estimates as meta-features for classifier selection.