Effective Class-Imbalance learning based on SMOTE and Convolutional Neural Networks. (arXiv:2209.00653v1 [cs.LG])

Imbalanced Data (ID) is a problem that deters Machine Learning (ML) models
for achieving satisfactory results. ID is the occurrence of a situation where
the quantity of the samples belonging to one class outnumbers that of the other
by a wide margin, making such models learning process biased towards the
majority class. In recent years, to address this issue, several solutions have
been put forward, which opt for either synthetically generating new data for
the minority class or reducing the …

arxiv class-imbalance convolutional neural networks networks neural networks smote