Experiments in Meta-level Learning with ILP

When considering new datasets for analysis with machine learning algorithms, we encounter the problem of choosing the algorithm which is best suited for the task at hand. The aim of meta-level learning is to relate the performance of diierent machine learning algorithms to the characteristics of the dataset. The relation is induced on the basis of empirical data about the performance of machine learning algorithms on the diierent datasets. In the paper, an Inductive Logic Programming (ILP) framework for meta-level learning is presented. The performance of three machine learning algorithms (the tree learning system C4.5, the rule learning system CN2 and the k-NN nearest neighbour classiier) were measured on twenty datasets from the UCI repository in order to obtain the dataset for meta-learning. The results of applying ILP on this meta-learning problem are presented and discussed.