Algorithm 1 Pseudocode of ensemble of classifiers as well as undersampling the negative set

Input:

P= Positive set (minority class samples in the dataset D)

N= Negative set (majority class samples in the dataset D)

M=Number of base learners in ensemble model

Output:

Ensemble = trained ensemble.

for $j\in \{\mathrm{ErG},\mathrm{ESPF}\}$ do

for  $i\in 1\to 8$ do

Randomly sample $N_i\in N$ : $\left|N_i\right|=\left|P\right|$

${\mathrm{Classifier}}_{i,j}$ = train classifier using P and $N_i$

end for

end for

$\mathrm{Ensemble}=\frac{1}{M}{\sum }_1^M\mathrm{Output}\mathrm{Probability}\mathrm{of}\mathrm{Each}\mathrm{Learner}$