Table 2.

Sampling methodologies

Sampling method	Brief description
Random over-sampling (Cernuda 2019; Maheshwari et al. 2011; Sun et al. 2011)	Arbitrary instances of the minority data set are selected at random
Synthetic Minority Oversampling Technique (SMOTE) (Wagner et al. 2016; Maheshwari et al. 2011)	Arbitrary instances of minority class through kNN
Random undersampling (Cernuda 2019; Sun et al. 2011)	Instances of the majority data set are removed at random
Hart’s condensed nearest neighbour rule (CNN) (Wagner et al. 2016)	Selection of a correctly classified set of majority class through a 1-kNN
Wilson’s edited nearest neighbour rule (ENN) (Wagner et al. 2016)	Removal of majority class data points through a 3-kNN approach