2026-05-23T19:36:39Zhttps://riubu.ubu.es/oai/request

oai:riubu.ubu.es:10259/109022026-04-22T22:42:17Zcom_10259_4219com_10259_5086com_10259_2604col_10259_7349

Martin-Melero, Íñigo Serrano Mamolar, Ana Rodríguez Diez, Juan José 2025-09-30T08:27:05Z 2025-09-30T08:27:05Z 2024-04-23 979-8-3503-0436-7 979-8-3503-0437-4 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 info:eu-repo/semantics/openAccess 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