## 2 Related Work
### SSL Methods  
[PERSON] et al. [11] employed an SSL approach to predict COVID-19 cases accurately from digital chest X-rays and Computed Tomography (CT) scans. Their proposed COVIDCon algorithm consisted of data augmentation (weak and strong), consistency regularization, and multi-contrastive learning. However, COVIDCon algorithm's classification is based on multi-contrastive learning, with no consideration of knowledge related to COVID-19 domain. Moreover, this algorithm performs classification of images instead of regression over numerical input values. No explanation is provided by COVIDCon in support of the predictive diagnosis of COVID-19. [PERSON] et al. [12] proposed a new multi-contrastive semi-supervised learning algorithm \"MultiCon\" to classify drug function into 12 categories by analyzing the features of two-dimensional images of drug chemical structure. This algorithm used multi-contrastive loss with consistency regularization to predict drug function. However, this algorithm's multi-contrastive learning approach distinguishes image features through clustering. It does not take into account domain knowledge of drug function. Moreover, this algorithm is restricted to image classification only rather than numerical data regression. \"MultiCon\" also does not explain the rationale in support of its predictive output on drug function. [PERSON] et al. [53] proposed a new semi-supervised learning methodology Mixup Contrastive Mixup (MCoM) to address the imbalanced data problem in tabular cyber security datasets. To tackle this imbalance, their proposed MCoM employed triplet mixup data augmentation on the minority (vulnerable) class. Then they used contrastive and feature reconstruction loss to train the encoder and the decoder. They used a label propagation technique to pseudo-label the subset of the unlabeled data. For the predictor, they used Multi-Layer Perceptron (MLP). However, MLP is a data-driven approach, with no consideration of knowledge of cyber security domain. MCoM performs classification of security data instead of regression. Moreover, security classification output is not explained by MCoM. [PERSON] et al. [54] employed an evolutionary multi-objective algorithm, the SPEA2, for semi-supervised training of Least Squares Support Vector Machine (LSSVM) with both labeled and unlabeled data. However, LSSVM is a data-driven approach, with no consideration of domain knowledge. This paper performs binary classification with LSSVM instead of regression. Moreover, classification output is not explained by LSSVM, resulting in a lack of explainability of the output to the end user. [PERSON] et al. [55] proposed a model to perform classification with K-nearest neighbor (KNN). For semi-supervised learning of KNN, they proposed a synthetic, labeled data generation method called Diverse Training Dataset Generation based on a Multi-objective Optimization for Semi-Supervised Classification (DTGMO-SSC). However, KNN is a data-driven approach with no domain knowledge. Moreover, classification output is not explained by this method. [PERSON] et al. [56] proposed Evolutionary Optimization-based Pseudo Labeling (EOPL) for semi-supervised soft sensor development of industrial processes. They employed Gaussian Process Regression (GPR) as the base learner. They enlarged the labeled training dataset by combining the high-confidence pseudo-labeled data with initial labeled data. Then, the GPR model was rebuilt with this enlarged dataset. They also extended EOPL to ensemble EOPL (EnEOPL) for enhanced prediction performance. However, do main knowledge is not taken into account by GPR. Moreover, EOPL and EnEOPL do not explain the predictive output. The diversity of the unlabeled data in feature space is also not emphasized in this research. [PERSON] et al. [57] proposed the Evolutionary Multi-Tasking Semi-Supervised Classification (EMT-SSC) method by combining Support Vector Machine (SVM) with a modified Z-score. However, SVM does not take into account domain knowledge.