Meta-learning for resampling recommendation systems

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

    Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

    13 Citations (Scopus)

    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.

    Original languageEnglish
    Title of host publicationEleventh International Conference on Machine Vision, ICMV 2018
    EditorsDmitry P. Nikolaev, Antanas Verikas, Jianhong Zhou, Petia Radeva
    PublisherSPIE
    ISBN (Electronic)9781510627482
    DOIs
    Publication statusPublished - 2019
    Event11th International Conference on Machine Vision, ICMV 2018 - Munich, Germany
    Duration: 1 Nov 20183 Nov 2018

    Publication series

    NameProceedings of SPIE - The International Society for Optical Engineering
    Volume11041
    ISSN (Print)0277-786X
    ISSN (Electronic)1996-756X

    Conference

    Conference11th International Conference on Machine Vision, ICMV 2018
    Country/TerritoryGermany
    CityMunich
    Period1/11/183/11/18

    Keywords

    • binary classification
    • imbalanced datasets
    • meta-learning
    • resampling method selection
    • resampling recommendation systems

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