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Machine Learning Methods for Fear Classification Based on Physiological Features
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 substan...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271969/ https://www.ncbi.nlm.nih.gov/pubmed/34282759 http://dx.doi.org/10.3390/s21134519 |
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author | Petrescu, Livia Petrescu, Cătălin Oprea, Ana Mitruț, Oana Moise, Gabriela Moldoveanu, Alin Moldoveanu, Florica |
author_facet | Petrescu, Livia Petrescu, Cătălin Oprea, Ana Mitruț, Oana Moise, Gabriela Moldoveanu, Alin Moldoveanu, Florica |
author_sort | Petrescu, Livia |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-8271969 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-82719692021-07-11 Machine Learning Methods for Fear Classification Based on Physiological Features Petrescu, Livia Petrescu, Cătălin Oprea, Ana Mitruț, Oana Moise, Gabriela Moldoveanu, Alin Moldoveanu, Florica Sensors (Basel) Article 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. MDPI 2021-07-01 /pmc/articles/PMC8271969/ /pubmed/34282759 http://dx.doi.org/10.3390/s21134519 Text en © 2021 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Petrescu, Livia Petrescu, Cătălin Oprea, Ana Mitruț, Oana Moise, Gabriela Moldoveanu, Alin Moldoveanu, Florica Machine Learning Methods for Fear Classification Based on Physiological Features |
title | Machine Learning Methods for Fear Classification Based on Physiological Features |
title_full | Machine Learning Methods for Fear Classification Based on Physiological Features |
title_fullStr | Machine Learning Methods for Fear Classification Based on Physiological Features |
title_full_unstemmed | Machine Learning Methods for Fear Classification Based on Physiological Features |
title_short | Machine Learning Methods for Fear Classification Based on Physiological Features |
title_sort | machine learning methods for fear classification based on physiological features |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8271969/ https://www.ncbi.nlm.nih.gov/pubmed/34282759 http://dx.doi.org/10.3390/s21134519 |
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