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Authors: Rui Leite and Pavel Brazdil

Affiliation: LIACC/FEP, University of Porto, Portugal

Abstract: We present a method that can be seen as an improvement of standard progressive sampling method. The method exploits information concerning performance of a given algorithm on past datasets, which is used to generate predictions of the stopping point. Experimental evaluation shows that the method can lead to significant time savings without significant losses in accuracy.

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Paper citation in several formats:
Leite, R. and Brazdil, P. (2004). A Meta-learning Approach to Improve Progressive Sampling. In Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS; ISBN 972-8865-01-5, SciTePress, pages 25-34. DOI: 10.5220/0002668800250034

@conference{pris04,
author={Rui Leite. and Pavel Brazdil.},
title={A Meta-learning Approach to Improve Progressive Sampling},
booktitle={Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS},
year={2004},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002668800250034},
isbn={972-8865-01-5},
}

TY - CONF

JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems (ICEIS 2004) - PRIS
TI - A Meta-learning Approach to Improve Progressive Sampling
SN - 972-8865-01-5
AU - Leite, R.
AU - Brazdil, P.
PY - 2004
SP - 25
EP - 34
DO - 10.5220/0002668800250034
PB - SciTePress