Publikation

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 Seiten 69-73 online 2012.

Abstrakt

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.

ki2012pd15.pdf (pdf, 759 KB )

Deutsches Forschungszentrum für Künstliche Intelligenz
German Research Center for Artificial Intelligence