Journal
SENSORS
Volume 21, Issue 13, Pages -Publisher
MDPI
DOI: 10.3390/s21134519
Keywords
emotion dimensions; emotion classification; fear classification; neural networks; machine learning
Funding
- Romanian Ministry of Research and Innovation, CCCDI-UEFISCDI within PNCDI III [43PTE/2020]
- UEFISCDI [1/2018]
- UPB CRC Research Grant 2017
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This paper focuses on binary classification of fear emotion using physiological data and subjective responses. By extracting relevant features and optimizing machine learning algorithms, along with interpreting results using various methods, the study achieved stable and reliable classification accuracy.
This paper focuses on the binary classification of the emotion of fear, based on the physiological data and subjective responses stored in the DEAP dataset. We performed a mapping between the discrete and dimensional emotional information considering the participants' ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms-Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks-accompanied by dimensionality reduction, feature selection and the tuning of the most relevant hyperparameters, boosting classification accuracy. The methodology we approached included tackling different situations, such as resolving the problem of having an imbalanced dataset through data augmentation, reducing overfitting, computing various metrics in order to obtain the most reliable classification scores and applying the Local Interpretable Model-Agnostic Explanations method for interpretation and for explaining predictions in a human-understandable manner. The results show that fear can be predicted very well (accuracies ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine) by extracting the most relevant features from the physiological data and by searching for the best parameters which maximize the machine learning algorithms' classification scores.
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