Algorithm 1WAM Algorithm:

Given a training dataset $\mathbb{X}$ with p features, a testing dataset $\mathbb{T},$ a feature selection method $FS,$ a threshold parameter $k,$ and a learning algorithm $M.$ (i) For $s=1,\dots ,m,$ generate bootstrap samples, ${\mathbb{X}}_1,\dots ,{\mathbb{X}}_m$ of the training dataset $\mathbb{X}.$ (ii) Based on $FS$, get the features score matrix $\mathbb{L}.$ (iii) Get the aggregated score set $\{{\overline{\ell }}_{.1},\dots ,{\overline{\ell }}_{.p}\}.$ (iv) For the aggregated score set $\{{\overline{\ell }}_{.1},\dots ,{\overline{\ell }}_{.p}\},$ get the corresponding rank vector $\mathit{r}=(r_1,\dots ,r_p).$ (v) Based on the rank vector $\mathit{r},$ keep only the top $100k\%$ of the variable set $\{V_1,\dots ,V_p\}.$ (vi) Based on the selected feature set in (v), use the testing dataset $\mathbb{T}$ and a cross-validation technique to train and test the model $M.$