A Comprehensive Dataset for Evaluating Approaches of Various Meta-learning Tasks

New approaches in pattern recognition are typically evaluated against standard datasets, e.g. from UCI or StatLib. Using the same and publicly available datasets increases the comparability and reproducibility of evaluations. In the field of meta-learning, the actual dataset for evaluation is created based on multiple other datasets. Unfortunately, no comprehensive dataset for meta-learning is currently publicly available. In this paper, we present a novel and publicly available dataset for meta-learning based on 83 datasets, six classification algorithms, and 49 meta-features. Different target variables like accuracy and training time of the classifiers as well as parameter dependent measures are included as ground-truth information. Therefore, the meta-dataset can be used for various meta-learning tasks, e.g. predicting the accuracy and training time of classifiers or predicting the optimal parameter values. Using the presented meta-dataset, a convincing and comparable evaluation of new meta-learning approaches is possible.