## 4 Experimental Methodology
### Performance Evaluation of WvEnSL against Ensemble Self-Labeled Algorithms
Since our motivation stems from the fact that we are interested in evaluating the rejection of the hypothesis that all the algorithms perform equally well for a given level based on their classification accuracy and highlighting the existence of significant differences between our proposed algorithm and the classical self-labeled algorithms, we utilized the non-parametric Friedman Aligned Ranking (FAR) [37] test. \begin{table}
\begin{tabular}{p{113.8 pt} p{113.8 pt} p{113.8 pt}} \hline \hline
**Algorithm** & **Parameters** \\ \hline SMO & Supervised base learner & \(C=1.0\), Tolerance parameter \(=0.001\), Pearson VII function-based kernel, Epsilon \(=1.0\times 10^{-12}\), Fit logistic models \(=\) true. \\ \hline C4.5 & Supervised base learner & Confidence level: \(c=0.25\), Minimum number of item-sets per leaf: \(i=2\), Prune after the tree building. \\ \hline \(k\)NN & Supervised base learner & Number of neighbors \(=3\), Euclidean distance. \\ \hline Self-training & Self-labeled (single classifier) & MaxIter \(=40\), \(c=95\%\). \\ \hline Co-training & Self-labeled (multiple classifier) & MaxIter \(=40\), Initial unlabeled pool \(=75\) \\ \hline Tri-training & Self-labeled (multiple classifier) & No parameters specified. \\ \hline Co-Bagging & Self-labeled (multiple classifier) & Committee members \(=3\), Ensemble learning \(=\) Bagging. \\ \hline Democratic-Co & Self-labeled (multiple classifier) & Classifiers \(=k\)NN, C4.5, NB. \\ \hline CST-Voting & Ensemble of self-labeled & No parameters specified. \\ \hline DTCo & Ensemble of self-labeled & No parameters specified. \\ \hline EnSL & Ensemble of self-labeled & No parameters specified. \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Parameter specification for all the base learners and self-labeled methods used in the experimentation. \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline \multirow{2}{*}{**Algorithm**} & \multicolumn{2}{c}{**Ratio = 10\%**} & \multicolumn{2}{c}{**Ratio = 20\%**} & \multicolumn{2}{c}{**Ratio = 30\%**} & \multicolumn{2}{c}{**Ratio = 40\%**} \\ \cline{2-9}  & \(\mathbf{F_{1}}\) & \(\mathbf{Acc}\) & \(\mathbf{F_{1}}\) & \(\mathbf{Acc}\) & \(\mathbf{F_{1}}\) & \(\mathbf{Acc}\) & \(\mathbf{F_{1}}\) & \(\mathbf{Acc}\) \\ \hline CST-Voting (SMO) & 66.67\% & 66.00\% & 70.00\% & 70.00\% & 73.08\% & 72.00\% & 75.00\% & 74.00\% \\ WvEnSL\({}_{1}\) (SMO) & **68.00\%** & **68.00\%** & **71.29\%** & **71.00\%** & **73.79\%** & **73.00\%** & **75.73\%** & **75.00\%** \\ \hline CST-Voting (C4.5) & 67.96\% & 67.00\% & 71.84\% & 71.00\% & 73.79\% & 73.00\% & 73.79\% & 73.00\% \\ WvEnSL\({}_{1}\) (C4.5) & **69.90\%** & **69.00\%** & **73.79\%** & **73.00\%** & **75.00\%** & **74.00\%** & **75.73\%** & **75.00\%** \\ \hline CST-Voting (\(k\)NN) & 66.00\% & 66.00\% & 69.90\% & 69.00\% & 73.79\% & 73.00\% & 73.27\% & 73.00\% \\ WvEnSL\({}_{1}\) (\(k\)NN) & **66.67\%** & **67.00\%** & **70.59\%** & **70.00\%** & **72.00\%** & **72.00\%** & **74.75\%** & **75.00\%** \\ \hline DTCo & 66.02\% & 65.00\% & 69.90\% & 69.00\% & 72.55\% & 72.00\% & 74.29\% & 73.00\% \\ WvEnSL\({}_{2}\) & **67.33\%** & **67.00\%** & **71.29\%** & **71.00\%** & **72.55\%** & **72.00\%** & **76.92\%** & **76.00\%** \\ \hline EnSL & 64.08\% & 63.00\% & 71.84\% & 71.00\% & 74.29\% & 73.00\% & 74.29\% & 73.00\% \\ WvEnSL\({}_{3}\) & **69.90\%** & **69.00\%** & **75.73\%** & **75.00\%** & **76.47\%** & **76.00\%** & **77.67\%** & **77.00\%** \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Performance evaluation of WvEnSL against ensemble self-labeled algorithms for CT Medical dataset.