Abstract
Arguably, model selection is one of the major obstacles, and a key once solved, to the widespread use of machine learning/data mining technology in business. Landmarking is a novel and promising metalearning approach to model selection. It uses accuracy estimates from simple and effcient learners to describe tasks and subsequently construct meta-classifers that predict which one of a set of more elaborate learning algorithms is appropriate for a given problem. Experiments show that landmarking compares favourably with the traditional statistical approach to meta-learning.
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Bensusan, H., Giraud-Carrier, C. (2000). Discovering Task Neighbourhoods through Landmark Learning Performances. In: Zighed, D.A., Komorowski, J., Żytkow, J. (eds) Principles of Data Mining and Knowledge Discovery. PKDD 2000. Lecture Notes in Computer Science(), vol 1910. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45372-5_32
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DOI: https://doi.org/10.1007/3-540-45372-5_32
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