The most common method to handle the class imbalance is data resampling that involves either over-sampling minority class instances or under-sampling majority class instances. In the case of under ...

In this work, we propose to improve the classification accuracy in the case of imbalanced training data by equally balancing a training data set using a hybrid approach which consists in over-sampling ...

At first, we remove the noisy data using Tomek-Links. After that we create several balanced subsets by applying random under sampling (RUS) method to the majority ... We also use 27 data-sets with ...

Class imbalance in available microbiome data is one ... were implemented using the imbalanced-learn Python toolbox (v.0.6.1) (Lemaître et al., 2017) with default parameters. We employed a combination ...