Key result
Machine learning algorithms predict fear with up to ~94% accuracy using Support Vector Machines.
Why the study?
Accurate classification of fear based on physiological data requires effective machine learning methods and feature selection to improve prediction performance.
Machine learning algorithms, particularly Support Vector Machine with dimensionality reduction, can accurately classify the emotion of fear based on physiological features.
Answers are grounded in this paper and cite their sources.
ML-based fear classification from physiology is feasible; leaves open prospective validation before any clinical adoption.
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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Petrescu et al. (2021) studied Emotion of fear. Machine learning algorithms was evaluated on Classification accuracy of fear. Machine learning algorithms predicted fear with high accuracy, ranging from 91.7% using Gradient Boosting Trees to 93.5% using dimensionality reduction and Support Vector Machine.
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