Algorithm 1: Bagging Algorithm [49]

Input:   $S^{+}$: Training set with positive samples   $S^{-}$: Training set with negative samples   T: Number of iterations n: The size of a random selection    $\mathrm{I}$: Weak classifier 1:for $t=1$ to T do 2:$S_t^{-}=$ Random Sample Selection $(n,S^{-})$ 3:$h_t=\mathrm{I}\left(S_t^{-}\cup S^{+}\right)$ 4:end for Output:   Bagged classifier:$H\left(x\right)=\mathrm{sgn}\left({\sum }_{t=1}^T{h}_t\left(x\right)\right)$ where $h_t\in \left[-1,1\right]$