2026-06-10T19:10:28Zhttps://riubu.ubu.es/oai/requestoai:riubu.ubu.es:10259/109022026-04-22T22:42:17Zcom_10259_4219com_10259_5086com_10259_2604col_10259_7349
Repositorio Institucional de la Universidad de Burgos
author
Martin-Melero, Íñigo
author
Serrano Mamolar, Ana
author
Rodríguez Diez, Juan José
2025-09-30T08:27:05Z
2025-09-30T08:27:05Z
2024-04-23
979-8-3503-0436-7
https://hdl.handle.net/10259/10902
10.1109/PerComWorkshops59983.2024.10502901
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.
eng
Machine learning
Semi-supervised learning
Affective computing
Python
R
Evaluation of Semi-Supervised Machine Learning applied to Affective State Detection
info:eu-repo/semantics/conferenceObject
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URL
https://riubu.ubu.es/bitstream/10259/10902/1/Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf
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application/pdf
Martin-Evaluation_Semi-Supervised_Machine_Learning_applied_Affective_State_Detection_2024.pdf