Meta2-Features: Providing Meta-Learners More Information

Meta-features are used to describe properties and characteristics of datasets and construct the feature space for meta-learning. Many of the different meta-features are defined for single variables and, therefore, are computed per feature of the dataset. Since datasets contain different numbers of features but meta-learning requires feature vectors of the same size, such measures are typically simply averaged over all columns. In this paper, we present an approach of preserving more information of such meta-features while producing a feature vector with a fixed size. An additional level of features are extracted from the meta-features.