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Proceedings Paper

Meta-learning for resampling recommendation systems
Author(s): Dmitry Smolyakov; Alexander Korotin; Pavel Erofeev; Artem Papanov; Evgeny Burnaev
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Paper Abstract

One possible approach to tackle the class imbalance in classification tasks is to resample a training dataset, i.e., to drop some of its elements or to synthesize new ones. There exist several widely-used resampling methods. Recent research showed that the choice of resampling method significantly affects the quality of classification, which raises the resampling selection problem. Exhaustive search for optimal resampling is time-consuming and hence it is of limited use. In this paper, we describe an alternative approach to the resampling selection. We follow the meta-learning concept to build resampling recommendation systems, i.e., algorithms recommending resampling for datasets on the basis of their properties.

Paper Details

Date Published: 15 March 2019
PDF: 13 pages
Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411S (15 March 2019); doi: 10.1117/12.2523103
Show Author Affiliations
Dmitry Smolyakov, Skolkovo Institute of Science and Technology (Russian Federation)
Alexander Korotin, Skolkovo Institute of Science and Technology (Russian Federation)
Pavel Erofeev, Moscow Institute of Physics and Technology (Russian Federation)
Artem Papanov, Moscow Institute of Physics and Technology (Russian Federation)
Evgeny Burnaev, Skolkovo Institute of Science and Technology (Russian Federation)


Published in SPIE Proceedings Vol. 11041:
Eleventh International Conference on Machine Vision (ICMV 2018)
Antanas Verikas; Dmitry P. Nikolaev; Petia Radeva; Jianhong Zhou, Editor(s)

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