Abstract
The rapid growth of HCI applications results in increased data size and complexity. For this, advanced machine learning techniques and data analysis solutions are used to prepare and process data patterns. However, the cost of data pre-processing, labelling, and classification can be significantly increased if the dataset is huge, complex, and unlabelled. This paper aims to propose a data pre-processing approach and semi-supervised learning technique to prepare and classify a big Motion Capture Hand Postures dataset. It builds the solutions via Tri-training and Co-forest techniques and compares them to figure out the best-fitted approach for hand posture classification. According to the results, Co-forest outperforms Tri-training in terms of Accuracy, Precision, recall, and F1-score.
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Nan, K., Hu, S., Luo, H., Wong, P., Pourroostaei Ardakani, S. (2023). A Semi-supervised Learning Application for Hand Posture Classification. In: Hou, R., Huang, H., Zeng, D., Xia, G., A. Ghany, K.K., Zawbaa, H.M. (eds) Big Data Technologies and Applications. BDTA BDTA 2022 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 480. Springer, Cham. https://doi.org/10.1007/978-3-031-33614-0_10
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