RT info:eu-repo/semantics/conferenceObject
T1 Evaluation of Semi-Supervised Machine Learning applied to Affective State Detection
A1 Martin-Melero, Íñigo
A1 Serrano Mamolar, Ana
A1 Rodríguez Diez, Juan José
K1 Machine learning
K1 Semi-supervised learning
K1 Affective computing
K1 Python
K1 R
K1 Aprendizaje automático
K1 Machine learning
K1 Emociones y sentimientos
K1 Emotions
AB 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.
PB IEEE
SN 979-8-3503-0436-7
YR 2024
FD 2024-04-23
LK https://hdl.handle.net/10259/10902
UL https://hdl.handle.net/10259/10902
LA eng
NO 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.
NO 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.
DS Repositorio Institucional de la Universidad de Burgos
RD 15-may-2026