# Special Issue on Ensemble Learning and Applications
## 2 Ensemble Learning and Applications  
The first paper is entitled _\"A Weighted Voting Ensemble Self-Labeled Algorithm for the Detection of Lung Abnormalities from X-Rays\"_ and it is authored by [PERSON] et al. [5]. The authors presented a new ensemble-based semi-supervised learning algorithm for the classification of lung abnormalities from chest X-rays. The proposed algorithm exploits a new weighted voting scheme which assigns a vector of weights on each component learner of the ensemble based on its accuracy on each class. The proposedalgorithm was extensively evaluated on three famous real-world benchmarks, namely the Pneumonia chest X-rays dataset from Guangzhou Women and Children's Medical Center, the Tuberculosis dataset from Shenzhen Hospital and the cancer CT-medical images dataset. The presented numerical experiments showed the efficiency of the proposed ensemble methodology against simple voting strategy and other traditional semi-supervised methods. The second paper is authored by [PERSON] et al. [6] entitled _\"Exploring an Ensemble of Methods that Combines Fuzzy Cognitive Maps and Neural Networks in Solving the Time Series Prediction Problem of Gas Consumption in Greece\"_. This paper presents an innovative ensemble time-series forecasting model for the prediction of gas consumption demand in Greece. The model is based on an ensemble learning technique which exploits evolutionary Fuzzy Cognitive Maps (FCMs), Artificial Neural Networks (ANNs) and their hybrid structure, named FCM-ANN, for time-series prediction. The prediction performance of the proposed model was compared against that of the Long Short-Term Memory (LSTM) model on three time-series datasets concerning data from distribution points which compose the natural gas grid of a Greek region. The presented results illustrated empirical evidence that the proposed approach could be effectively utilized to forecast gas consumption demand. The third paper _\"A Grey-Box Ensemble Model Exploiting Black-Box Accuracy and White-Box Intrinsic Interpretability\"_ was written by [PERSON] et al. [7]. In this interesting study, the authors proposed a new framework for the development of a Grey-Box machine learning model based on the semi-supervised philosophy. The advantages of the proposed model are that it is nearly as accurate as a Black-Box and it is also interpretable like a White-Box model. More specifically, in their proposed framework, a Black-Box model was utilized for enlarging a small initial labeled dataset, adding the model's most confident predictions of a large unlabeled dataset. In the sequel, the augmented dataset was utilized for training a White-Box model which greatly enhances the interpretability and explainability of the final model (ensemble). For evaluating the flexibility as well as the efficiency of the proposed Grey-Box model, the authors used six benchmarks from three real-world application domains, i.e., finance, education, and medicine. Based on their detailed experimental analysis the authors stated that the proposed model reported comparable and sometimes better prediction accuracy compared to that of a Black-Box while being at the same time interpretable as a White-Box model. The fourth paper was authored by [PERSON] et al. [8] entitled _\"A Soft-Voting Ensemble Based Co-Training Scheme Using Static Selection for Binary Classification Problems\"_. The authors presented an ensemble-based co-training scheme for binary classification problems. The proposed methodology is based on the imposition of an ensemble classifier as a base learner in the co-training framework. Its structure is determined by a static ensemble selection approach from a pool of candidate learners Their experimental results in a variety of classical benchmarks as well as the reported statistical analysis showed the efficacy and efficiency of their approach. An interesting research entitled _\"GeoAI: A Model-Agnostic Meta-Ensemble Zero-Shot Learning Method for Hyperspectral Image Analysis and Classification_ was authored by [PERSON] and [PERSON] [9].