# Precision-Based Weighted Blending Distributed Ensemble Model for Emotion Classification
## 5 Conclusions  
For emotion categorization, a weighted blended distributed ensemble model is presented in this study. The proposed strategy is tested on three datasets (FER-2013, CK+, and FERG-DB), as well as on their combination. For emotion classification, the proposed network employs the SparkML pipeline approach. Transfer learning is utilized to extract features from emotion facial images. The proposed ensemble model network outperforms the competition as it considers not only the probabilities of each class, but also the precision value of each classifier, when generating the final prediction, thus giving greater weight to the classifier that performs well throughout each run. SPARK distributed computing is used in the proposed technique, so that it can work efficiently and effectively on any large dataset. In the intermediate step of the ensemble, the proposed approach can be extended by using any number of classifiers. Our future directions will focus on applying the model for real-time face emotion classification in video sequences.  
Conceptualization, M.V.J.; methodology, G.S. and M.V.J.; formal analysis and investigation, G.S., M.V.V. and M.V.J.; writing--original draft preparation, G.S. and M.V.V.; writing--review and editing, G.S., M.V.V., M.V.J., E.P. and V.C.G.; supervision: M.V.J., E.P. and V.C.G.; project administration, V.C.G. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Figure 8: Confusion matrix on the combined dataset.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**SNO** & **Dataset** & **Precision \%** & **Recall \%** & **F1-Score \%** \\ \hline
1 & FER-2013 & 77.01 & 76.95 & 75.9 \\
2 & CK+ & 98.96 & 98.47 & 98.68 \\
3 & FERG-DB & 99.5 & 99.57 & 99.5 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Precision, recall and F1-score values on the three datasets.  
Data Availability Statement: Three datasets were analyzed in this study which are available upon request for non-commercial use. In particular, the FER-2013 dataset is available at: [[https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)]([https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data](https://www.kaggle.com/c/challenges-in-representation-learning-facial-expression-recognition-challenge/data)) (accessed on 31 December 2021). The FER-DB dataset is available at: [[http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html](http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html)]([http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html](http://grail.cs.washington.edu/projects/deepexpr/ferg-2d-db.html)) (accessed on 31 December 2021). The CK+ dataset is available at: [[http://www.jeffcohn.net/Resources/](http://www.jeffcohn.net/Resources/)]([http://www.jeffcohn.net/Resources/](http://www.jeffcohn.net/Resources/)) (accessed on 31 December 2021).