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Minimax Modifications of Linear Discriminant Analysis for Classification with Rare Classes

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dc.contributor.author Bratanova K.
dc.contributor.author Kareev I.
dc.contributor.author Salimov R.
dc.date.accessioned 2021-02-25T06:54:50Z
dc.date.available 2021-02-25T06:54:50Z
dc.date.issued 2020
dc.identifier.uri https://dspace.kpfu.ru/xmlui/handle/net/161487
dc.description.abstract © 2020 IEEE. We consider the problem of classification for imbalanced samples with rare classes. A common problem for machine learning methods in such setting is that a rare class would have extremely high classification error compared to more widespread classes. In general, this problem could be mitigated with re-sampling or fitting additional weights to control the classification errors in classes, though those methods are computationally expensive for large datasets and sometimes fail to attain appropriate results. It this paper we present cost-efficient modifications of Linear Discriminant Analysis allowing to mitigate the problem by minimizing maximal classification error among the classes. For example, this allows achieving more robust machinery malfunction detection algorithms where our expectations on recall would be more consistent among different malfunction types.
dc.subject classification
dc.subject imbalanced sample dataset
dc.subject linear discriminant analysis
dc.subject minimax error
dc.subject rare class
dc.title Minimax Modifications of Linear Discriminant Analysis for Classification with Rare Classes
dc.type Conference Paper
dc.collection Публикации сотрудников КФУ
dc.source.id SCOPUS-2020-SID85096417870


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  • Публикации сотрудников КФУ Scopus [24551]
    Коллекция содержит публикации сотрудников Казанского федерального (до 2010 года Казанского государственного) университета, проиндексированные в БД Scopus, начиная с 1970г.

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