Meta-learning for resampling recommendation systems

Dmitry Smolyakov; Alexander Korotin; Pavel Erofeev; Artem Papanov; Evgeny Burnaev

doi:10.1117/12.2523103

15 March 2019 Meta-learning for resampling recommendation systems

Dmitry Smolyakov, Alexander Korotin, Pavel Erofeev, Artem Papanov, Evgeny Burnaev

Proceedings Volume 11041, Eleventh International Conference on Machine Vision (ICMV 2018); 110411S (2019) https://doi.org/10.1117/12.2523103
Event: Eleventh International Conference on Machine Vision (ICMV 2018), 2018, Munich, Germany

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.

Citation Download Citation

Dmitry Smolyakov, Alexander Korotin, Pavel Erofeev, Artem Papanov, and Evgeny Burnaev "Meta-learning for resampling recommendation systems", Proc. SPIE 11041, Eleventh International Conference on Machine Vision (ICMV 2018), 110411S (15 March 2019); https://doi.org/10.1117/12.2523103

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