# Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
## 2 Related Work
This classification method proved successful in extracting more accurate facial expressions. [PERSON] et al. (2019) look at the role of ensemble learning in emotion classification from both visual and linguistic perspectives. They employed five state-of-the-art classifiers as independent models: naive Bayes (NB), Bayesian network (BN), closest nearest-neighbor (NN), decision tree (DT) and linear support vector machine (SVM). To assess the predictions provided by all these different models, the authors considered two distinct ensemble methodologies based on the Bayesian model averaging method (BMA). A study of low-level and mid-level features in convolutional neural networks (CNNs) for facial expression identification was undertaken by [PERSON] et al. (2019). They suggested a model that included three different forms of mid-level links as part of an ensemble. The results revealed that the ensemble model can achieve a final classification accuracy of 74.09% in facial representation. They also used a method for face emotion identification that concatenated feature vectors from three multi-level networks, followed by fully connected layers. For face expression recognition, [PERSON] et al. (2019) also presented an ensemble approach. Multi-region ensemble CNN is the approach employed in this study (MRE-CNN). The final recognition rate can be improved over the initial single network utilizing a weighted sum operation of the prediction scores from each subnetwork. The suggested work is assessed using the databases AFEW 7.0 and RAF-DB. The authors in ([PERSON] et al., 2019) suggested a foreground extraction-based FER system based on an Xception model. The foreground extraction-based FER technique extracts the FER features reliably, whereas the system's deep learning model successfully uses these features for model training. The Xception model, utilized in this study, makes the most of the foreground extracted face images from the FER dataset and improves FER performance. In ([PERSON] et al., 2019), a feature vector extraction technique is introduced, which integrates facial landmarks into facial image pixel values, while the deep learning model employs these combined features as input. The FER deep learning model efficiently uses facial landmark characteristics to classify user emotions with the least amount of classification error. Compared to the previous approaches, when generating the final prediction for emotion classification, not only does the proposed ensemble network consider the probabilities of each class, but also the precision value of each classifier, giving more weight to the classifier that performs well throughout each run and thus improving performance. As the amount of data grows, processing such a vast amount of data becomes harder, which may have an impact on the model's performance. So, in the proposed approach, we used the SparkML ([PERSON] et al., 2019) pipeline, which enhances the model's performance even more. It is common in machine learning to run a series of algorithms to process and learn from data. A pipeline in Spark ML is a process that consists of a series of pipeline stages (transformers and estimators) that must run in a specified order. The proposed model is described in the next section.