Dataset Generation for Meta-Learning
Meta-learning tries to improve the learning process by using knowledge about already completed learning tasks. Therefore, features of dataset, so-called meta-features, are used to represent datasets. These meta-features are used to create a model of the learning process. In order to make this model more predictive, sufficient training samples and, thereby, sufficient datasets are required. In this paper, we present a novel data-generator that is able to create datasets with specified meta-features, e.g., it is possible to create datasets with specific mean kurtosis and skewness. The publicly available data-generator uses a genetic approach and is able to incorporate arbitrary meta-features.