# Special Issue on Ensemble Learning and/or Explainability
## 2 Ensemble Learning and/or Explainability  
The first paper in this Special Issue is entitled \"_The Study of Multiple Classes Boosting Classification Method Based on Local Similarity_\" and it is authored by [PERSON] and [PERSON] [2]. This paper presents a new approach to cross-modal retrieval based on a simple enhancement framework which utilizes local similarity and it is extended to multimodal, multiclass enhancement Boosting. To achieve mutual retrieval of text and images, local similarity is utilized for the development of weak learners and a Boosting technique is used to create a powerful ensemble prediction model. The authors theoretically proved that in their proposed framework, the training loss is exponentially minimized. Additionally,they conducted a series of experiments using two co-opened multimodal databases (NUS-WIDE and Wiki) to verify and evaluate the performance of their proposed algorithm. Their experimental results illustrated that their approach has considerable advantages for cross-mode retrieval compared to traditional methods. The second paper is entitled \"_Ensembling EfficientNets for the Classification and Interpretation of Histopathology Images_\" and it is authored by [PERSON] et al. [3]. In this work, the authors proposed an ensemble scheme for the classification of histopathology breast and colon cancer images. In more detail, they implemented an ensemble classifier based on the state-of-the-art EfficientNets networks, which was combined with a Grad-CAM explanation scheme used to highlight the visual patterns responsible for each class prediction. The presented numerical experiments provided empirical evidence that the proposed approach is able to implement a robust, interpretable, and explainable histopathology image classifier. Furthermore, the authors developed a standalone application (web service) following the principles of distributed computing for online evaluation, validation, and experimentation. The third paper is entitled \"_A Rule Extraction Technique Applied to Ensembles of Neural Networks, Random Forests, and Gradient-Boosted Trees_\". [PERSON] [4] highlighted the considerable disadvantage of neural network ensemble models in explaining their responses, namely their lack of interpretability or transparency with regard to how the input data are processed to model's outputs. In this work, a new rule extraction method is presented which is able to locate the discriminating hyperplanes that constitute the antecedents of the decision rules. The proposed rule extraction method was applied to decision tree and multilayer perceptron ensembles, which were evaluated based on eight classification benchmarks from UCI repository. The presented experimental analysis showed that the proposed ensembles reported competitive performance with that of \"Skope-Rules\" [5] and other rule extraction techniques with respect to the characteristics of the generated rules. The fourth paper is entitled \"_Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification_\" and it is authored by [PERSON] et al. [6]. In this study, the authors conduct emotion classification by exploring the capabilities of a distributed ensemble-based model utilizing precision-based weighted blending. The proposed model works in a distributed manner using the SparkML pipeline [7], which enhances the model's performance. The considerable advantages of this approach are the effectiveness of iterative computations and the model's quick in-memory processing capabilities. The experimental analysis included three well-known emotional benchmarks (the FER-2013 image dataset, the extended Cohn-Kanade facial expression dataset, and the FERG-DB dataset). Based on their numerical experiments, the authors stated that the proposed ensemble model network outperforms other state-of-the-art models, as it exploits the probabilities of each class along with the precision value of each classifier. The fifth paper is entitled \"_Prediction of Injuries in CrossFit Training: A Machine Learning Perspective_\".