Reduction of Large Training Set by Guided Progressive Sampling: Application to Neonatal Intensive Care Data
François Portet, Feng Gao, Jim Hunter, René Quiniou · 2007
Although large training sets are supposed to improve the performance of learning algorithms, there are limits to the volume of data such an algorithm can handle. To overcome this problem, we describe an improvement to a progressive sampling method by guiding the construction of a reduced training set. The application of this method to neonatal intensive care data shows that it is possible to reduce a training set to a third of its original size without decreasing performance. 1.