## 6 Experimental Process and Results
Although some further modifications of the internal parameters of each learner were investigated, as well as the combination of same learning models, but distinct regressors per view (e.g., train GB(ls) on \(\mathbf{L}(F_{1})\) and train GB(huber) on \(\mathbf{L}(F_{2})\)), but neither this factserves our ambitions nor any great improvement was achieved. More information could be found in Reference [41]. As it concerns the rest required information about our evaluations, we set _Max_iter_ equal to 100 and the performance metric f \(\equiv\) MSE (Mean Squared Error). Moreover, we applied a 5-fold-Cross-Validation (5-fold-CV) evaluation process, while we held 100 instances out of the 1073 as the test set. Consequently, the rest \(n\) = 973 instances constitute the \(D\) set, where the size of the \(L\) (\(l\)) and the \(U\) (\(u\)) subsets sum up to \(n\). Thus, we examined four different values of the initially labeled instances: 50, 100, 150, and 200, while all of the rest instances were exploited from the first iteration as the \(U\) subset, since, as already mentioned, a possible random sampling of the total \(U\) subset per iteration did not favor us. Finally, the scenario under which our selectors exploit kNN algorithm with (k\({}_{1}\), k\({}_{2}\)) = (1,1) did not manage to detect instances that satisfy the restriction of consistency as described in Equation (3) in the majority of the conducted experiments, and for this reason, was excluded by our results. The performance of the examined COREG variants based on the mean absolute error (MAE) metric is presented in Tables 4 and 5. To be more specific, in these tables, we have recorded the relative improvement between the performance of each regressor during the initially provided labeled set, and the iteration that recorded the best performance until the criterion of either exceeding the _Max_iter_ or not satisfying the consistency is violated. The results indicate that there is a decrease in the MAE metric, whilst the number of labeled instances is increased, as could be expected. Based only on the information regarding the first semester, it is noticed that the best performers are LR and MLP for size(\(L\)) = 50, while the tree-based learners achieved a more stable improvement over all the examined initially labeled subsets. Based on the information regarding both the first and the second semester, it is observed that the best performers are again LR and MLP for size(\(L\)) = 50, while they also performed greater improvement in the rest of the examined scenarios against their behavior on the previous case. Additionally, we observe that as the cardinality of the \(L\) subset increases, the relative improvement of the investigated multi-view SSR approaches is decreasing in both cases during the majority of the recorded results. Through this kind of information, we can understand better the benefits of SSR approaches like COREG when multi-view problems are considered even under both limited labeled data are provided, and the volume of the unlabeled data is also highly restricted, reducing, thus the informativeness of this source of knowledge which is crucial for SSL scenario. Hence, the most important asset of transforming the COREG approach into a multi-view SSR variant is the remarkable reduction of the mean absolute error under strict conditions regarding the initially provided labeled instances. Despite the fact that the supervised learning performance in that cases is usually poor, since it heavily depends on the initially labeled data, both the insights that are obtained through the distinct, independent views and the disagreement mechanism that interchanges information between regressors that are fitted to these views lead to superior performance against it. Therefore, we believe that this indication is our most important contribution: Proof that in a real-life scenario, the complementary behavior of two separate views can be a trustworthy solution--even under highly limited labeled instances and not a large pool of unlabeled ones.