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OFAI-TR-2002-36 ( 154kB PDF file)

On the Use of Fast Subsampling Estimates for Algorithm Recommendation

Johannes Fürnkranz, Johann Petrak, Pavel Brazdil, Carlos Soares

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. The main goal of the paper is to investigate the influence of various parameter choices on the meta-learning performance, in particular the size of training and test sets and the number of subsamples.

Citation: Fürnkranz J., Petrak J., Brazdil P., Soares C.: On the Use of Fast Subsampling Estimates for Algorithm Recommendation. Technical Report, Österreichisches Forschungsinstitut für Artificial Intelligence, Wien, TR-2002-36, 2002