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OFAI-TR-2001-13 ( 50kB g-zipped PostScript file,  206kB PDF file)

An Evaluation of Landmarking Variants

Johannes Fürnkranz, Johann Petrak

Landmarking is a novel technique for data characterization in meta-learning. While conventional approaches typically describe a database with its statistical measurements and properties, landmarking proposes to enrich such a description with quick and easy-to-obtain performance measures of simple learning algorithms. In this paper, we will discuss two novel aspects of landmarking. First, we investigate relative landmarking, which tries to exploit the relative order of the landmark measures instead of their absolute value. Second, we propose to the use of subsampling estimates as a different way for efficiently obtaining landmarks. In general, our results are mostly negative. The most interesting result is a surprisingly simple rule that predicts quite accurately when it is worth to boost decision trees.

Keywords: Meta-Learning, Landmarking, Subsampling

Citation: Fürnkranz J., Petrak J.: An Evaluation of Landmarking Variants. In C. Giraud-Carrier, N. Lavrac, S. Moyle & B. Kavsek (eds.) Proceedings of the ECML/PKDD-01 Workshop Integrating Aspects of Data Mining, Decision Support and Meta-learning, pp.57-68, Freiburg, Germany, 2001.