On the Use of Fast Subsampling Estimates for Algorithm Recommendation
Johannes Fürnkranz, Johann Petrak, Pavel Brazdil, Carlos Soares · 2002
The use of subsampling for scaling up the performance of learning algorithms has become fairly popular in the recent literature. In this paper, we investigate the use of performance estimates obtained on a subsample of the data for the task of recommending the best learning algorithm(s) for the problem. In particular, we examine the use of subsampling estimates as features for meta-learning, thereby generalizing previous work on landmarking and on direct algorithm recommendation via subsampling.