## 3 The Proposed Co-Training Scheme
Table 6 includes the computed Critical

Figure 1: Pie chart depicting the participation of each combination inside our Static Ensemble strategy. Difference (CD) which is the same for all the cases of this R-based scenario (CD = 2.27). It is statistically confirmed that the difference between the \(Co(Vote_{soft}^{SEC})\) algorithm and the majority of the other methods is statistically significant in all examined metrics, thus verifying the predominance of the proposed co-training scheme. The fact also that the proposed algorithm outperforms the Vote (all) variants means that the implemented time-efficient SEC strategy provides a more accurate base learner for the field of SSL. Towards this direction, we visualize the performance of the proposed algorithm against \(Co(Vote(all))\) for the examined metrics and the case of R = 90% via a violin plot which favors the comparison of the distribution of the achieved values per algorithm, including also some important statistical quantities: median, interquartile range, and 1.5\(\times\) interquartile range (Figure 2). Therefore, we can deduce experimentally the success of the proposed approach, especially when generic binary datasets constitute the main issue to be tackled when the collected labeled instances are highly numerically restricted. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multirow{2}{*}{**Dataset**} & \multicolumn{5}{c}{**Algorithms**} \\ \cline{2-6}  & \(Co(Vote_{soft}^{SEC})\) & \(Co(Vote(all))\) & \(Co(Self(all))\) & \(Co(LR)\) & \(Co(Vote(DT,LR))\) \\ \hline bands & \(0.668\pm 0.082\) & \(0.668\pm 0.051\) & \(0.649\pm 0.036\) & \(0.627\pm 0.036\) & \(0.657\pm 0.084\) \\ breast & \(0.679\pm 0.065\) & \(0.696\pm 0.056\) & \(0.7\pm 0.038\) & \(0.693\pm 0.056\) & \(0.686\pm 0.114\) \\ bupa & \(0.571\pm 0.052\) & \(0.603\pm 0.085\) & \(0.529\pm 0.041\) & \(0.537\pm 0.042\) & \(0.591\pm 0.057\) \\ chess & \(0.966\pm 0.013\) & \(0.948\pm 0.007\) & \(0.955\pm 0.008\) & \(0.947\pm 0.011\) & \(0.962\pm 0.01\) \\ colic.ORIG & \(0.759\pm 0.055\) & \(0.757\pm 0.044\) & \(0.724\pm 0.077\) & \(0.73\pm 0.053\) & \(0.768\pm 0.036\) \\ diabetes & \(0.773\pm 0.038\) & \(0.777\pm 0.032\) & \(0.738\pm 0.048\) & \(0.765\pm 0.03\) & \(0.681\pm 0.059\) \\ h-statlog & \(0.719\pm 0.036\) & \(0.7\pm 0.027\) & \(0.707\pm 0.051\) & \(0.77\pm 0.034\) & \(0.656\pm 0.08\) \\ kr-vs-kp & \(0.966\pm 0.019\) & \(0.947\pm 0.008\) & \(0.953\pm 0.008\) & \(0.946\pm 0.012\) & \(0.963\pm 0.012\) \\ mammographic & \(0.788\pm 0.019\) & \(0.796\pm 0.023\) & \(0.793\pm 0.024\) & \(0.787\pm 0.025\) & \(0.78\pm 0.039\) \\ monk-2 & \(1\pm 0\) & \(0.918\pm 0.019\) & \(0.88\pm 0.047\) & \(0.784\pm 0.029\) & \(1\pm 0\) \\ pima & \(0.691\pm 0.02\) & \(0.683\pm 0.025\) & \(0.686\pm 0.038\) & \(0.697\pm 0.032\) & \(0.652\pm 0.033\) \\ saheart & \(0.715\pm 0.021\) & \(0.713\pm 0.067\) & \(0.702\pm 0.036\) & \(0.728\pm 0.02\) & \(0.672\pm 0.038\) \\ sick & \(0.976\pm 0.003\) & \(0.967\pm 0.005\) & \(0.963\pm 0.006\) & \(0.95\pm 0.004\) & \(0.978\pm 0.004\) \\ tic-tac-toe & \(0.804\pm 0.07\) & \(0.805\pm 0.038\) & \(0.735\pm 0.037\) & \(0.824\pm 0.036\) & \(0.796\pm 0.057\) \\ vote & \(0.914\pm 0.026\) & \(0.891\pm 0.026\) & \(0.916\pm 0.024\) & \(0.889\pm 0.02\) & \(0.889\pm 0.033\) \\ wdbc & \(0.958\pm 0.021\) & \(0.963\pm 0.01\) & \(0.972\pm 0.012\) & \(0.956\pm 0.021\) & \(0.942\pm 0.031\) \\ wisconsin & \(0.974\pm 0.006\) & \(0.98\pm 0.01\) & \(0.972\pm 0.011\) & \(0.97\pm 0.016\) & \(0.936\pm 0.039\) \\ \hline \hline \end{tabular} Bold highlighted means the best value per dataset.

\end{table} 
Table 3: Classification accuracy (\(\pm\)stdev) values for the best five variants (labeled ratio 10%). Figure 2: Violin plots of the proposed algorithm against \(Co(Vote(all))\) approach over the three examined metrics.