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Experiment Databases: Creating a New Platform for Meta-Learning Research

Abstract
Many studies in machine learning try to investigate what makes an algorithm succeed or fail on certain datasets. However, the field is still evolving relatively quickly, and new algorithms, preprocessing methods, learning tasks and evaluation procedures continue to emerge in the literature. Thus, it is impossible for a single study to cover this expanding space of learning approaches. In this paper, we propose a community-based approach for the analysis of learning algorithms, driven by sharing meta-data from previous experiments in a uniform way. We illustrate how organizing this information in a central database can create a practical public platform for any kind of exploitation of meta-knowledge, allowing effective reuse of previous experimentation and targeted analysis of the collected results.
Type
Conference Contribution
Type of thesis
Series
Citation
Vanschoren, J, Blockeel, H, Pfahringer, B & Holmes, G. (2008). Experiment databases: creating a new platform for meta-learning research. In P. Brazdil, A. Bernstein & L. Hunter (Eds) Proceedings of ICML/COLT/UAI 2008 Planning to Learn Workshop (PlanLearn). Helsinki, Finland, 9 July, 2008(pp.10-15). Helsinki, Finland: University of Porto.
Date
2008
Publisher
University of Porto
Degree
Supervisors
Rights
This article has been published in the Proceedings of ICML/COLT/UAI 2008 Planning to Learn Workshop (PlanLearn). Used with Permission.