# Special Issue on Ensemble Learning and Applications
## 2 Ensemble Learning and Applications
In this work, a new classification model was proposed, named MAME-ZsL (Model-Agnostic Meta-Ensemble Zero-shot Learning), which is based on zero-shot philosophy for geographic object-based scene classification. The attractive advantages of the proposed model are its training stability, its low computational cost, but mostly its remarkable generalization performance thought the reduction of potential overfitting. This is performed by the selection of features which do not cause the gradients to explode or diminish. Additionally, it is worth noticing that the superiority of MAME-ZsL model lies on the fact that the testing set contained instances whose classes were not contained in the training set. The effectiveness of the proposed architecture was presented against state-of-the-art fully supervised deep learning models on two datasets containing images from a reflective optics system imaging spectrometer. [PERSON] and [PERSON] [10] presented a research paper entitled _\"Ensemble Learning of Hybrid Acoustic Features for Speech Emotion Recognition\"_. Signal processing and machine learning methods are widely utilized for recognizing human emotions based on extracted features from video files,facial images or speech signals. The authors studied the problem that many classification models were not able to efficiently recognize fear emotion with the same level of accuracy as other emotions. To address this problem, they proposed an elegant methodology for improving the precision of fear and other emotions recognition from speech signals, based on an interesting feature extraction technique. In more detail, their framework extracts highly discriminating speech emotion feature representations from multiple sources which are subsequently agglutinated to form a new set of hybrid acoustic features. The authors conducted a series of experiments on two public databases using a variety of state-of-the-art ensemble classifiers. The presented analysis which reported the efficiency of their approach, provided evidence that the utilization of the new features increased the generalization ability of all ensemble classifiers. The seventh paper entitled \"_Ensemble Deep Learning for Multilabel Binary Classification of User-Generated Content_ is authored by [PERSON] et al. [11]. The authors presented an multilabel ensemble model for emotion classification which exploits a new weighted voting strategy based on differential evolution. Additionally, the proposed model used deep learning learners which comprised of convolutional and pooling layers as well as (LSTM) layers which are dedicated for such classification problems. To present the efficiency of their model, they conducted a performance evaluation, on two large and widely used datasets, against state-of-the-art single models and ensemble models which were comprised with the same base learners. The reported numerical experiments showed that the proposed model presented improved classification performance, outperforming state-of-the-art compared models. Finally, the eighty paper \"_Ensemble Deep Learning Models for Forecasting Cryptocurrency Time-Series_\" was authored by [PERSON] et al. [12]. The main contribution of this research is the combination of three of the most widely employed ensemble strategies: ensemble-averaging, bagging and stacking with advanced deep learning methodologies for forecasting the cryptocurrency hourly prices of Bitcoin, Etherium and Ripple. More analytically, the ensemble models utilized state-of-the-art deep learning models as component learners, which were comprised by combinations of LSTM, Bi-directional LSTM and convolutional layers. The authors conducted an exhaustive experimentation in which the performance of all ensemble deep learning models was compared on both regression and classification problems. The models were evaluated on forecasting of the cryptocurrency price on the next hour (regression) and also on the prediction of next price directional movement (classification) with respect to the current price.