A Meta-learning Approach to Improve Progressive Sampling

Rui Leite, Pavel Brazdil

2004

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 Harvard Style

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 - Volume 1: PRIS, (ICEIS 2004) ISBN 972-8865-01-5, pages 25-34. DOI: 10.5220/0002668800250034


in Bibtex Style

@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 - Volume 1: PRIS, (ICEIS 2004)},
year={2004},
pages={25-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002668800250034},
isbn={972-8865-01-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 4th International Workshop on Pattern Recognition in Information Systems - Volume 1: PRIS, (ICEIS 2004)
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