Dataset Generation for Meta-Learning

Matthias Reif; Faisal Shafait; Andreas Dengel
In: Stefan Wölfl (Hrsg.). KI-2012: Poster and Demo Track. German Conference on Artificial Intelligence (KI-12), 35th, September 24-27, Saarbrücken, Germany, Pages 69-73, online, 2012.


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.

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