Development of Metalearning Systems for Algorithm Recommendation

In the previous chapter, a metalearning approach to support the selection of learning algorithms was described. The approach was illustrated with a simple method that provides a recommendation concerning which algorithm to use on a given learning problem. The method predicts the relative performance of algorithms on a dataset based on their performance on datasets that were previously processed.

The development of metalearning systems for algorithm recommendation involves addressing several issues not only at the meta level (lower part of Figure 3.1) but also at the base level (top part of Figure 3.1). At the meta level, it is necessary, first of all, to choose the type of the target feature (or metatarget, for short), that is, the form of the recommendation that is provided to the user. In the system presented in the previous chapter, the form of recommendation adopted was rankings of base-algorithms. The type of metatarget determines the type of meta-algorithm, that is, the metalearning methods that can be used. This in turn determines the type of metaknowledge that can be obtained. The meta-algorithm described in the previous chapter was an adaptation of the k-nearest neighbors (k-NN) algorithm for ranking. The metatarget and the meta-algorithm are discussed in more detail in Section 3.2.