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Dataset Generation for Meta-Learning

Matthias Reif, Faisal Shafait, Andreas Dengel

KI-2012: Poster and Demo Track German Conference on Artificial Intelligence (KI-12), 35th, September 24-27, Saarbr├╝cken, 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.

Show BibTex:

@inproceedings {
       abstract = {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.},
       number = {}, 
       month = {}, 
       year = {2012}, 
       title = {Dataset Generation for Meta-Learning}, 
       journal = {}, 
       volume = {}, 
       pages = {69-73}, 
       publisher = {online}, 
       author = {Matthias Reif, Faisal Shafait, Andreas Dengel}, 
       keywords = {},
       url = {http://www.dfki.de/web/forschung/publikationen/renameFileForDownload?filename=ki2012pd15.pdf&file_id=uploads_1806}
}