Algorithm 4. The prediction phase of the PCC-FS algorithm

Input: original test data, $T\ast Q$ binary classifiers ${\mathrm{ƕ}}_{r,j},$ $r=1,2,\dots ,T;j=1,2,\dots ,Q$; Output: predicted label sets for ${\mathfrak{X}}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}}$; Steps generate ${\mathfrak{X}}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}}$ through feature selection process; initialize ${\chi }_{test}^{1,j}$ with zero matrix ${\left[\mathbf{0}\right]}_{\mathit{n}\times \mathit{Q}}$; for $r\in 1,2,\dots ,T$; for $j\in 1,2,\dots ,Q$; $\mathit{l}\mathit{p}\mathit{r}{\mathit{e}}^{\mathit{r},\mathit{j}}=\mathrm{Ƈ}\left({\mathrm{Ɣ}}_j\right)$; ${\mathit{\chi }}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}}^{\mathit{r},\mathit{j}}=\left[{\mathfrak{X}}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}},\mathit{l}\mathit{p}\mathit{r}{\mathit{e}}^{\mathit{r},\mathit{j}}\right]$; $\mathrm{Ƈ}\left(\mathit{j}\right)={\mathrm{ƕ}}_{r,j}\left({\mathit{\chi }}_{\mathit{t}\mathit{e}\mathit{s}\mathit{t}}^{\mathit{r},\mathit{j}}\right)$; end for; end for; return the predicted results $\mathrm{Ƈ}$.