## 3 The Proposed Co-Training Scheme
```  
**Algorithm 1**SSoftEC _strategy_  
```
Mode: Pool-based scenario over a provided dataset D = X\({}_{\text{n}\times\text{k}}\)\(\bigcup\) Y\({}_{\text{n}\times 1}\) x\({}_{\text{i}}\)--vector with k features \(<\)f\({}_{1}\), f\({}_{2}\),... f\({}_{\text{k}}\)\(>\)\(\forall\) 1 \(\leq\) i \(\leq\) n y\({}_{\text{i}}\)--scalar class variable with y\({}_{\text{i}}\in[0,1]\)\(\forall\) 1 \(\leq\) i \(\leq\) n {x\({}_{\text{i}}\), y\({}_{\text{i}}\)}--i-th labeled instance (I\({}^{\text{i}}\)) with 1 \(\leq\) i \(\leq\) n\({}_{\text{i}}\) {x\({}_{\text{i}}\)}--i-th unlabeled instance (u\({}^{\text{i}}\)) with 1 \(\leq\) i \(\leq\) n\({}_{\text{u}}\) F\({}_{\text{view}}\)--separate feature sets with view \(\in\)[1,2] learnerview--build of selected learner on corresponding View, Vview = 1, 2 Input: L\({}^{\text{iter}}\)--labeled instances during iter-th iteration, L\({}^{\text{iter}}\)\(\subset\) D U\({}^{\text{iter}}\)--unlabeled instances during iter-th iteration, U\({}^{\text{iter}}\)\(\subset\) D  iter--number of combined executed iterations MaxIter--maximum number of iterations C--pool of classifiers \(\equiv\) {SVM, kNN, DT, NB, LR} (f, \(\alpha\))--number of folds to split the validation set during SEC and value of Equation (4) Preprocess: k\({}^{\prime}\)--number of features after having converted each categorical feature into binary n\({}^{\prime}\)--number of instances after having removed instances with at least one missing value C\({}_{\text{j}}\)--instance cardinalities of both existing classes with j \(\in\) {min, max} Mined\({}_{\text{c}}\)--define number of mined instances per class, where c \(\in\) {class\({}_{0}\), class\({}_{1}\)}
```  
**Algorithm 2**_Ensemble based co-training variant_  
``` Input: L--labeled set f--number of folds to split the L C--pool of classification algorithms exporting class probabilities \(\alpha\)--value of balancing parameter Main Procedure: For each \(i,j\in 0,1,\ldots,|\text{C}|\) and \(i\
eq j\)do Set iter = 0, \(Q_{soft}^{a}(i,j)=0\) Split L t of separate folds: \(\left\{L^{(1)},L^{(2)},\ldots,\,L^{(f)}\right\}\) While iter \(\leq\) fdo Train C\({}_{i}\), C\({}_{j}\) on \(L\setminus L^{(iter)}\) Apply C\({}_{i}\), C\({}_{j}\) on \(L^{(iter)}\) Update\(Q_{soft}^{a}(i,j)\) according to Equation (4) \(iter=iter+1\) Output: Return pair of indices i, j such that: \((i,j)^{*}:arg\max_{i,j}Q_{soft}^{a}(i,j)\

## 4 Experimental Procedure and Results

For the purpose of our study a number of experiments were carried out using 27 benchmark datasets from UCI Machine Learning Repository [49] regarding binary classification problems (Table 1), where the sign # depicts the cardinality of the corresponding quantity. Note that the columns entitled # Features in Table 1, counts all the features apart from the class variable. These datasets have been partitioned into 10 equal-sized folds using the stratified 10-fold-CV resampling procedure so that each fold should have the same distribution as the entire dataset [50]. This process was repeated 10 times until all folds were used as the testing set and the results were averaged. Moreover, each fold was divided into two subsets, one labeled and the other one unlabeled, in accordance with a selected labeled ratio value (R) which is defined as follows:

\[R=|L_{D}|/(|L_{D}|+|U_{D}|). \tag{5}\]

In order to study the influence of the amount of labeled data in the training set, three different ratios were used, and in particular: \(10\%,20\%\), and \(30\%\). In general, the \(R\) (%) values over which researchers are interested are the smaller ones (\(R<50\%\)), so as to be consistent with the practical aspect of SSL scenario. The effectiveness of the proposed co-training scheme was compared to several co-training and self-training variants. For verifying the supremacy of the \(Vote_{soft}^{SEC}\) as base classifier, we built the soft-Voting versions based on all pairs of the inserted pool of classifiers (C). Furthermore, the version that exploits the decisions of all the participants of C pool was implemented, as well as the