The concept of meta-learning applies to the area of predictive data mining to combine the predictions from multiple models. It is particularly useful when the types of models included in the project are very different. In this context, this procedure is also referred to as Stacking (Stacked Generalization).

Suppose your data mining project includes tree classifiers, such as
C&RT and CHAID,
linear discriminant analysis (e.g., see GDA),
and Neural
Networks. Each computes predicted classifications for a cross-validation
sample, from which overall goodness-of-fit statistics (e.g., misclassification
rates) can be computed. Experience has shown that combining the predictions
from multiple methods often yields more accurate predictions than can
be derived from any one method (e.g., see Witten and Frank, 2000). The
predictions from different classifiers can be used as input into a meta-learner,
which will attempt to combine the predictions to create a final best predicted
classification. So, for example, the predicted classifications from the
tree classifiers, linear model, and the neural network classifier(s) can
be used as input variables into a neural network

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See also Data Mining.