PSO-Fuzzy-XGBoost-Classifier

PSO Fuzzy XGBoost Classifier Boosted with Neural Gas Features on EEG Signals in Emotion Recognition -
Link to the Paper:


Emotion recognition is the technology-driven process of identifying and categorizing human emotions from various data sources, such as facial expressions, voice patterns, body motion, and physiological signals, such as EEG. These physiological indicators, though rich in data, present challenges due to their complexity and variability, necessitating sophisticated feature selection and extraction methods. NGN, an unsupervised learning algorithm, effectively adapts to input spaces without predefined grid structures, improving feature extraction from physiological data. Furthermore, the incorporation of fuzzy logic enables the handling of fuzzy data by introducing reasoning that mimics human decision-making. The combination of PSO with XGBoost aids in optimizing model performance through efficient hyperparameter tuning and decision process optimization. This study explores the integration of Neural-Gas Network (NGN), XGBoost, Particle Swarm Optimization (PSO), and fuzzy logic to enhance emotion recognition using physiological signals. Our research addresses three critical questions concerning the improvement of XGBoost with PSO and fuzzy logic, NGN's effectiveness in feature selection, and the performance comparison of the PSO-fuzzy XGBoost classifier with standard benchmarks. Acquired results indicate that our methodologies enhance the accuracy of emotion recognition systems and outperform other feature selection techniques using the majority of classifiers, offering significant implications for both theoretical advancement and practical application in emotion recognition technology. In conclusion, our study has made significant strides in advancing the field of emotion recognition using physiological signals through the integration of Neural-Gas Network (NGN), XGBoost, Particle Swarm Optimization (PSO), and fuzzy logic. Our results affirm that the PSO-fuzzy XGBoost classifier not only enhances the accuracy of emotion recognition systems but also outperforms traditional classifiers and feature selection techniques. NGN's ability to effectively adapt to complex input spaces without predefined structures proves crucial in handling high-dimensional data and improving feature extraction, which is fundamental in physiological signal analysis. Furthermore, the incorporation of fuzzy logic facilitates the management of fuzzy data and introduces human-like reasoning capabilities into our models' decision-making processes. For future work, several avenues could be explored to further enhance emotion recognition systems. First, experimenting with larger and more varied datasets could help validate and refine the models, ensuring their robustness and applicability across different demographic and psychological profiles. Second, integrating multimodal data sources, such as combining physiological signals with facial expressions and voice patterns, could provide a more holistic approach to emotion recognition. Additionally, exploring other optimization algorithms alongside or in place of PSO could uncover more efficient ways to tune hyperparameters and optimize model performance. Lastly, delving deeper into the theoretical underpinnings of NGN and its potential synergies with other machine-learning approaches could unlock new methodologies for feature selection and classification in complex datasets. Through continued research and development, these enhancements could lead to more sophisticated, accurate, and accessible emotion recognition technologies, thereby expanding their practical applications in fields such as healthcare, marketing, automotive industries, and beyond.
In order to download the dataset: https://ieee-dataport.org/documents/brainwave-eeg-dataset