The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.
Efficient progressive sampling
Open accessAbstract
Having access to massive amounts of data does not necessarily imply that induction algorithms must use them all.Samples often provide the same accuracy with far less computational cost.However, the correct sample size rarely is obvious.We analyze methods for progressive samplingusing progressively larger samples as long as model accuracy improves.We explore several notions of efficient progressive sampling.We analyze efficiency relative to induction with all instances; we show that a simple, geometric sampling schedule is asymptotically optimal, and we describe how best to take into account prior expectations of accuracy convergence.We then describe the issues involved in instantiating an efficient progressive sampler, including how to detect convergence.Finally, we provide empirical results comparing a variety of progressive sampling methods.We conclude that progressive sampling can be remarkably efficient .
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Cites
Bellman, R. E. (2010) · doi:10.1515/9781400835386 · Cited by 14,588
Valiant, L. G. (1984) · Communications of the ACM · doi:10.1145/1968.1972 · Cited by 3,279
Cited by
Feurer, M., & Hutter, F. (2019) · The Springer Series on Challenges in Machine Learning · doi:10.1007/978-3-030-05318-5_1 · Cited by 1,414
Learning when training data are costly: the effect of class distribution on tree induction
Weiss, G. M., & Provost, F. (2003) · Journal of Artificial Intelligence Research · doi:10.1613/jair.1199 · Cited by 921
Self-citationAutomated machine learning: Review of the state-of-the-art and opportunities for healthcare
Waring, J., Lindvall, C., & Umeton, R. (2020) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2020.101822 · Cited by 834
Modern modelling techniques are data hungry: a simulation study for predicting dichotomous endpoints
van der Ploeg, T., Austin, P. C., & Steyerberg, E. W. (2014) · BMC Medical Research Methodology · doi:10.1186/1471-2288-14-137 · Cited by 809
“Some researchers aimed to develop a “power law” that can be used to determine the relation between sample size and the discriminatory ability of prediction models in terms of accuracy [8-10].”
BackgroundMethodA review of automatic selection methods for machine learning algorithms and hyper-parameter values
Luo, G. (2016) · Network Modeling Analysis in Health Informatics and Bioinformatics · doi:10.1007/s13721-016-0125-6 · Cited by 357
On Issues of Instance Selection
Liu, H., & Motoda, H. (2002) · Data Mining and Knowledge Discovery · doi:10.1023/a:1014056429969 · Cited by 191
Kalfarisi, R., Chew, A., Cai, J., Xue, M., Pok, J., & Wu, Z. Y. (2022) · Advances in Engineering Software · doi:10.1016/j.advengsoft.2022.103287 · Cited by 10
Metadata
- Authors
- Provost, F., Jensen, D., & Oates, T.
- Venue
- Proceedings of the fifth ACM SIGKDD international conference on Knowledge discovery and data mining
- Publisher
- ACM
- Published
- 1999-08
- Pages
- 23-32
- OpenAlex
- W1994326354
- Cited by
- 357
- References
- 2
- FWCI
- 13.30
- Type
- conference paper
- Language
- en
- Open access
- bronze
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