{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,14]],"date-time":"2026-03-14T17:40:02Z","timestamp":1773510002269,"version":"3.50.1"},"reference-count":61,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T00:00:00Z","timestamp":1625097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Romanian Ministery of Research and Innovation CCCDI\u2014UEFISCDI","award":["43PTE\/2020"],"award-info":[{"award-number":["43PTE\/2020"]}]},{"DOI":"10.13039\/501100006595","name":"UEFISCDI","doi-asserted-by":"publisher","award":["1\/2018, UPB CRC Research Grant 2017"],"award-info":[{"award-number":["1\/2018, UPB CRC Research Grant 2017"]}],"id":[{"id":"10.13039\/501100006595","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2019 ratings and extracted a substantial set of 40 types of features from the physiological data, which represented the input to various machine learning algorithms\u2014Decision Trees, k-Nearest Neighbors, Support Vector Machine and artificial networks\u2014accompanied 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\u2019 classification scores.<\/jats:p>","DOI":"10.3390\/s21134519","type":"journal-article","created":{"date-parts":[[2021,7,1]],"date-time":"2021-07-01T12:03:27Z","timestamp":1625141007000},"page":"4519","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":28,"title":["Machine Learning Methods for Fear Classification Based on Physiological Features"],"prefix":"10.3390","volume":"21","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9264-1603","authenticated-orcid":false,"given":"Livia","family":"Petrescu","sequence":"first","affiliation":[{"name":"Faculty of Biology, University of Bucharest, 050095 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7867-1117","authenticated-orcid":false,"given":"C\u0103t\u0103lin","family":"Petrescu","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]},{"given":"Ana","family":"Oprea","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6822-2684","authenticated-orcid":false,"given":"Oana","family":"Mitru\u021b","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3842-9828","authenticated-orcid":false,"given":"Gabriela","family":"Moise","sequence":"additional","affiliation":[{"name":"Faculty of Letters and Sciences, Petroleum-Gas University of Ploiesti, 100680 Ploiesti, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1368-7249","authenticated-orcid":false,"given":"Alin","family":"Moldoveanu","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8357-5840","authenticated-orcid":false,"given":"Florica","family":"Moldoveanu","sequence":"additional","affiliation":[{"name":"Faculty of Automatic Control and Computers, University Politehnica of Bucharest, 060042 Bucharest, Romania"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"101646","DOI":"10.1016\/j.bspc.2019.101646","article-title":"A machine learning model for emotion recognition from physiological signals","volume":"55","author":"Delahoz","year":"2020","journal-title":"Biomed. 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