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- W1597974927 abstract "In this paper we compare classifier selection using cross-validation with meta-learning, using as metafeatures 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 metafeatures for classifier selection." @default.
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- W1597974927 date "2012-11-01" @default.
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- W1597974927 title "Improving cross-validation based classifier selection using meta-learning" @default.
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