## 4 Experimental Methodology
### Performance Evaluation of WvEnSL against Ensemble Self-Labeled Algorithms  
Next, we focus our interest on the experimental analysis for evaluating the classification performance of WvEnSL algorithm against the ensemble self-labeled algorithms CST-Voting and DTCo, which utilize simple voting methodologies. It is worth noticing that our main goal is to measure the effectiveness of the proposed weighted voting strategy over the simple majority voting; therefore, we will compare ensembles using identical set of classifiers. This will eliminate the source of discrepancy originated from unequal classifiers. Thus, the difference in accuracy can solely be attributed to the difference of voting methodologies. Furthermore, the base learners utilized in all self-labeled algorithms are the Sequential Minimum Optimization (SMO) [33], the C4.5 decision tree algorithm [34] and the \(k\)NN algorithm [35] as in [2; 7; 8; 9], which probably constitute the most effective and popular machine learning algorithms for classification problems [36]. * \"CST-Voting (SMO)\" stands for an ensemble of Co-training, Self-training and Tri-training with SMO as base learner using majority voting [2]. * \"WvEnSL\({}_{1}\) (SMO)\" stands for Algorithm WvEnSL using the same components classifiers as CST-Voting (SMO). * \"CST-Voting (C4.5)\" stands for an ensemble of Co-training, Self-training and Tri-training with C4.5 as base learner using majority voting [2]. * \"WvEnSL\({}_{1}\) (C4.5)\" stands for Algorithm WvEnSL using the same components classifiers as CST-Voting (C4.5). * \"CST-Voting (\(k\)NN)\" stands for an ensemble of Co-training, Self-training and Tri-training with kNN as base learner using majority voting [2]. * \"WvEnSL\({}_{1}\) (\(k\)NN)\" stands for Algorithm WvEnSL using the same components classifiers as CST-Voting (\(k\)NN). * \"DTCo\" stands for an ensemble of Democratic-Co learning, Tri-training and Co-Bagging with C4.5 as base learner using majority voting [16]. * \"WvEnSL\({}_{2}\)\" stands for Algorithm WvEnSL using the same components classifiers as DTCo. * \"EnSL\" stands for an ensemble of Self-training, Democratic-Co learning, Tri-training and Co-Bagging with C4.5 as base learner using majority voting [17]. * \"WvEnSL\({}_{3}\)\" stands for Algorithm WvEnSL using the same components classifiers as EnSL. The configuration parameters for all supervised classifiers and self-labeled algorithms, utilized in our experiments, are presented in Table 1.  
Tables 2-4 presents the performance of all ensemble self-labeled methods on Pneumonia dataset, Tuberculosis dataset and CT Medical dataset, respectively. Notice that the highest classification performance for each ensemble of classifiers and performance metric is highlighted in bold. The aggregated results showed that the new weighted voting strategy exploits the individual predictions of each component classifier more efficiently than the simple voting schemes, illustrating better classification performance. WvEnSL\({}_{3}\) exhibits the best performance, reporting the highest \(F_{1}\)-score and accuracy, relative to all classification benchmarks and labeled ratio, followed by WvEnSL\({}_{2}\). In more detail, WvEnSL\({}_{3}\) demonstrates 82.53-83.49%, 69.79-71.73% and 69-77% classification accuracy for Pneumonia dataset, Tuberculosis dataset and CT Medical dataset, respectively; while WvEnSL\({}_{2}\) reports 81.89-83.17%, 69.79-71.55% and 67-77%, in the same situations. The statistical comparison of several classification algorithms over multiple datasets is fundamental in the area of machine learning and it is usually performed by means of a statistical test [2; 7; 8; 9].