2026-04-22T11:07:28Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/109022025-10-01T00:05:28Zcom_10259_4219com_10259_5086com_10259_2604col_10259_7349

Martin-Melero, Íñigo 8ca7e4d5-ab37-4b16-b0a9-7841c48a6284 500 Serrano Mamolar, Ana 820 600 0000-0002-0027-7128 Rodríguez Diez, Juan José 477 600 0000-0002-3291-2739 2025-09-30T08:27:05Z 2025-09-30T08:27:05Z 2024-04-23 2026-04-23 979-8-3503-0436-7 979-8-3503-0437-4 https://hdl.handle.net/10259/10902 10.1109/PerComWorkshops59983.2024.10502901 Comunicación presentada en: EmotionAware 2024: Eighth International Workshop on Emotion Awareness for Pervasive Computing Beyond Traditional Approaches, held as part of the 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), 11–15 March 2024, Biarritz, France. The affective computing field usually concerns data that is difficult, expensive or time-consuming to label. One way to overcome this limitation is the application of Semi-Supervised Machine Learning, that typically works with a small set of labeled data and a larger one of unlabeled data. This paper assesses the suitability of these techniques on the prediction of affective state, by analyzing the physiological and emotional response data of 30 different subjects while watching several emotion-eliciting videos. Three Semi-Supervised Learning algorithms are compared with their Supervised base classifiers in both a subject-independent and subject-dependent analyses, across a widely extended dataset. In view of the results, it can be concluded that Semi-Supervised Learning did not outperform their respective Supervised base classifiers for this specific dataset as it was expected. Subject-dependent classification resulted in accuracy rates between 68% and 85%, whereas the accuracy rates were between 38% and 72% for subject-independent classification. This work has been supported by the Junta de Castilla y Leon under project BU055P20 (JCyL/FEDER, UE), by the Ministry of Science and Innovation under project PID2020-119894GB-I00, co-financed through European Union FEDER funds, and by project TED2021-129485B-C43 funded by MCIN/AEI/10.13039/501100011033 and the European Union “NextGenerationEU”/PRTR. application/pdf eng IEEE 2024 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops), p. 320-325 https://doi.org/10.1109/PerComWorkshops59983.2024.10502901 info:eu-repo/grantAgreement/Junta de Castilla y León//BU055P20//Métodos y Aplicaciones Industriales del Aprendizaje Semisupervisado/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020/PID2020-119894GB-I00/ES/APRENDIZAJE AUTOMATICO CON DATOS ESCASAMENTE ETIQUETADOS PARA LA INDUSTRIA 4.0/ info:eu-repo/grantAgreement/AEI/Plan Estatal de Investigación Científica, Técnica y de Innovación 2021-2023/TED2021-129485B-C43/ES/Sistemas dinámicos inteligentes centrados en el usuario para la Prevención de Riesgos Laborales/ Machine learning Semi-supervised learning Affective computing Python R Aprendizaje automático Emociones y sentimientos Machine learning Emotions Evaluation of Semi-Supervised Machine Learning applied to Affective State Detection info:eu-repo/semantics/conferenceObject info:eu-repo/semantics/acceptedVersion info:eu-repo/semantics/embargoedAccess 320 325 THUMBNAIL Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf.jpg Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf.jpg IM Thumbnail image/jpeg 4539 https://riubu.ubu.es/bitstream/10259/10902/3/Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf.jpg 4c9125e4c2664a41999a7c720929d71e MD5 3 LICENSE license.txt license.txt text/plain; charset=utf-8 1362 https://riubu.ubu.es/bitstream/10259/10902/2/license.txt 5d013bfa6e473ff0db22cd82a4d71a70 MD5 2 ORIGINAL Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf application/pdf 1015672 https://riubu.ubu.es/bitstream/10259/10902/1/Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf 173c411c6f2dbd3fd5e9c58f51159343 MD5 1 10259/10902 oai:riubu.ubu.es:10259/10902 2025-10-01 02:05:28.139 Repositorio Institucional de la Universidad de Burgos bubrep@ubu.es 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