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Article

Machine Learning Methods for Fear Classification Based on Physiological Features

1
Faculty of Biology, University of Bucharest, 050095 Bucharest, Romania
2
Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania
3
Faculty of Letters and Sciences, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania
*
Author to whom correspondence should be addressed.
Academic Editors: Wataru Sato, Aime Lay-Ekuakille, Cosimo Ieracitano, Maryam Doborjeh and Mufti Mahmud
Sensors 2021, 21(13), 4519; https://doi.org/10.3390/s21134519
Received: 15 May 2021 / Revised: 29 June 2021 / Accepted: 29 June 2021 / Published: 1 July 2021
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. View Full-Text
Keywords: emotion dimensions; emotion classification; fear classification; neural networks; machine learning emotion dimensions; emotion classification; fear classification; neural networks; machine learning
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MDPI and ACS Style

Petrescu, L.; Petrescu, C.; Oprea, A.; Mitruț, O.; Moise, G.; Moldoveanu, A.; Moldoveanu, F. Machine Learning Methods for Fear Classification Based on Physiological Features. Sensors 2021, 21, 4519. https://doi.org/10.3390/s21134519

AMA Style

Petrescu L, Petrescu C, Oprea A, Mitruț O, Moise G, Moldoveanu A, Moldoveanu F. Machine Learning Methods for Fear Classification Based on Physiological Features. Sensors. 2021; 21(13):4519. https://doi.org/10.3390/s21134519

Chicago/Turabian Style

Petrescu, Livia, Cătălin Petrescu, Ana Oprea, Oana Mitruț, Gabriela Moise, Alin Moldoveanu, and Florica Moldoveanu. 2021. "Machine Learning Methods for Fear Classification Based on Physiological Features" Sensors 21, no. 13: 4519. https://doi.org/10.3390/s21134519

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