Handling Imbalanced Data Classification With Variational Autoencoding And Random Under-Sampling Boosting
In this thesis, a comparison of three different pre-processing methods for imbalanced classification data, is conducted. Variational Autoencoder, Random Under-Sampling Boosting and a hybrid approach of the two, are applied to three imbalanced classification data sets with different class imbalances....
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| Format: | Others |
| Language: | English |
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Uppsala universitet, Statistiska institutionen
2020
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| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-412838 |