A Semi-supervised Learning Application for Hand Posture Classification

Nan, Kailiang, Hu, Shengnan, Luo, Haozhe , Wong, Patricia and Pourroostaei Ardakani, Saeid (2023) A Semi-supervised Learning Application for Hand Posture Classification. In: 12th International Conference on Big Data Technologies and Applications.

Full content URL: https://doi.org/10.1007/978-3-031-33614-0_10

A Semi-supervised Learning Application for Hand Posture Classification
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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.

Keywords:Semi-supervised learning, Big data Analysis, Co-forest, Tri-training, hand posture classification
Subjects:G Mathematical and Computer Sciences > G760 Machine Learning
G Mathematical and Computer Sciences > G440 Human-computer Interaction
Divisions:College of Science > School of Computer Science
ID Code:54872
Deposited On:01 Jun 2023 14:37

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