Improving progressive sampling via meta-learning

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. © Springer-Verlag Berlin Heidelberg 2003.