Dataset Generation for Meta-Learning

Matthias Reif, Faisal Shafait, Andreas Dengel
In: Stefan Wölfl (ed.) KI-2012: Poster and Demo Track, Pages 69-73, Saarbrücken, Germany, online, 2012

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.

Files:

  ki2012pd15.pdf

BibTex:

@inproceedings{ REIF2012,
	Title = {Dataset Generation for Meta-Learning},
	Author = {Matthias Reif and Faisal Shafait and Andreas Dengel},
	Editor = {Stefan Wölfl},
	BookTitle = {KI-2012: Poster and Demo Track},
	Year = {2012},
	Publisher = {online},
	Pages = {69-73}
}

     
Last modified:: 30.08.2016