Abstract
We present a method that can be seen as an improvement of standard progressive sampling method. The method exploits information concerning performance of a given algorithm on past datasets, which is used to generate predictions of the stopping point. Experimental evaluation shows that the method can lead to significant time savings without significant losses in accuracy.
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Leite, R., Brazdil, P. (2003). Improving Progressive Sampling via Meta-learning. In: Pires, F.M., Abreu, S. (eds) Progress in Artificial Intelligence. EPIA 2003. Lecture Notes in Computer Science(), vol 2902. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-24580-3_37
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DOI: https://doi.org/10.1007/978-3-540-24580-3_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-20589-0
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