## 3 The Proposed Co-Training Scheme
\begin{table}
\begin{tabular}{c c c c c} \hline \hline \multicolumn{4}{c}{**Friedman Ranking**} \\ \hline
**Acc** & \multicolumn{2}{c}{**F\({}_{1}\)-Score**} & \multicolumn{2}{c}{**AUC**} \\ \hline
**Algorithm** & **Rank** & **Algorithm** & **Rank** & **Algorithm** & **Rank** \\ \hline \(Co(Vote^{SEC}_{soft})\) & 9.96 & \(Co(Vote^{SEC}_{soft})\) & 10.43 & \(Co(Vote^{SEC}_{soft})\) & 10.43 \\ \(Co(Vote(all))\) & 11.59 & \(Co(Vote(all))\) & 11.29 & \(Co(Vote(DT,LR)\) & 11.33 \\ \(Self(Vote(all))\) & 13.19 & \(Co(Vote(DT,LR)\)) & 12.81 & \(Co(Vote(all))\) & 12.88 \\ \(Co(LR)\) & 13.36 & \(Self(Vote(all))\) & 13.28 & \(Co(Vote(kNN,LR)\) & 13.17 \\ \(Co(Vote(DT,LR))\) & 13.89 & \(Co(LR)\) & 13.34 & \(Self(Vote(all))\) & 13.66 \\ \(Co(Vote(SVM,LR))\) & 14.13 & \(Co(DT)\) & 13.44 & \(Co(Vote(DT,GNB))\) & 14.17 \\ \(Co(DT)\) & 14.41 & \(Co(Vote(SVM,DT))\) & 13.54 & \(Self(Vote(DT,LR)\) & 14.28 \\ \(Co(Vote(SVM,DT))\) & 14.58 & \(Co(Vote(DT,GNB))\) & 13.64 & \(Co(Vote(SVM,LR)\) & 14.98 \\ \(Co(Vote(DT,GNB))\) & 14.58 & \(Self(Vote(DT,GNB))\) & 14.31 & \(Co(SVM)\) & 14.98 \\ \(Co(Vote(NN,LR))\) & 14.92 & \(Co(Vote(kNN,DT))\) & 14.54 & \(Co(LR)\) & 14.98 \\ \(Self(Vote(DT,GNB))\) & 15.33 & \(Co(Vote(kNN,LR))\) & 15.06 & \(Self(Vote(DT,GNB))\) & 15.24 \\ \(Self(LR)\) & 15.44 & \(Self(DT)\) & 15.12 & \(Co(Vote(GNB,LR))\) & 15.29 \\ \(Co(Vote(kNN,DT))\) & 15.64 & \(Self(LR)\) & 15.31 & \(Self(LR)\) & 16.01 \\ \(Self(Vote(SVM,LR))\) & 16.02 & \(Self(Vote(kNN,DT))\) & 15.46 & \(Self(Vote(kNN,LR)\) & 16.59 \\ \(Self(DT)\) & 16.04 & \(Self(Vote(DT,LR))\) & 15.78 & \(Co(Vote(kNN,DT)\) & 16.74 \\ \(Self(Vote(kNN,DT))\) & 16.61 & \(Self(Vote(SVM,DT))\) & 16.33 & \(Co(Vote(kNN,GNB))\) & 16.81 \\ \(Self(Vote(DT,LR))\) & 16.91 & \(Co(Vote(SVM,LR)\) & 16.33 & \(Co(Vote(SVM,DT)\) & 16.84 \\ \(Self(Vote(kNN,LR))\) & 16.96 & \(Co(Vote(kNN,GNB))\) & 17.05 & \(Co(DT)\) & 16.84 \\ \(Co(Vote(SVM,kNN))\) & 17.49 & \(Self(Vote(kNN,LR))\) & 17.21 & \(Co(Vote(SVM,GNB))\) & 17.06 \\ \(Self(Vote(SVM,DT))\) & 17.56 & \(Self(Vote(SVM,LR))\) & 17.38 & \(Co(GNB)\) & 17.06 \\ \(Co(Vote(kNN,GNB))\) & 18.06 & \(Self(Vote(kNN,GNB))\) & 17.69 & \(Self(Vote(GNB,LR))\) & 17.59 \\ \(Co(kNN)\) & 18.18 & \(Co(kNN)\) & 17.95 & \(Self(Vote(SVM,LR)\) & 18.09 \\ \(Self(Vote(kNN,GNB))\) & 18.34 & \(Co(Vote(SVM,kNN))\) & 19.20 & \(Self(SVM)\) & 18.09 \\ \(Co(Vote(SVM,GNB))\) & 19.45 & \(Co(Vote(SVM,GNB))\) & 19.59 & \(Self(Vote(kNN,GNB))\) & 18.42 \\ \(Self(Vote(SVM,knN))\) & 19.46 & \(Self(Vote(SVM,GNB))\) & 19.83 & \(Self(Vote(kNN,DT)\) & 18.54 \\ \(Self(Vote(SVM,GNB))\) & 20.01 & \(Self(Vote(GNB,LR))\) & 19.88 & \(Self(Vote(SVM,GNB))\) & 18.62 \\ \(Self(Vote(GNB,LR))\) & 20.29 & \(Co(Vote(GNB,LR))\) & 20.02 & \(Co(Vote(SVM,ANN)\) & 19.41 \\ \(Co(Vote(GNB,LR))\) & 20.43 & \(Self(kNN)\) & 21.06 & \(Co(kNN)\) & 19.41 \\ \(Co(SVM)\) & 20.74 & \(Self(GNB)\) & 21.15 & \(Self(GNB)\) & 19.52 \\ \(Self(kNN)\) & 20.85 & \(Self(Vote(SVM,kNN))\) & 21.61 & \(Self(Vote(SVM,DT)\) & 21.03 \\ \(Self(SVM)\) & 21.89 & \(Co(GNB)\) & 21.84 & \(Self(Vote(SVM,kNN))\) & 22.62 \\ \(Self(GNB)\) & 22.14 & \(Co(SVM)\) & 24.21 & \(Self(kNN)\) & 24.67 \\ \(Co(GNB)\) & 22.56 & \(Self(SVM)\) & 25.33 & \(Self(DT)\) & 25.67 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: [PERSON] Rankings for all examined algorithms and statistical importance based on Nemenyi post-hoc test. ## 5 Conclusions

In the present study, a soft-Voting ensemble-based co-training scheme through a Static Selection strategy operating under a random feature split, called \(Co(Vote_{so/t}^{SEC})\), was presented regarding binary classification problems. The proposed algorithm harnesses the benefits of the SSL approach and the ensemble methods that are built using heterogeneous approaches [61; 62; 63]. The experimental results using 27 benchmark datasets demonstrate the prevalence of the proposed algorithm in terms of classification accuracy and F\({}_{1}\)-score, compared to several co-training and self-training variants, while using five different labeled ratios.