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Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings

Research Article

A Semi-supervised Learning Application for Hand Posture Classification

Cite
BibTeX Plain Text
  • @INPROCEEDINGS{10.1007/978-3-031-33614-0_10,
        author={Kailiang Nan and Shengnan Hu and Haozhe Luo and Patricia Wong and Saeid Pourroostaei Ardakani},
        title={A Semi-supervised Learning Application for Hand Posture Classification},
        proceedings={Big Data Technologies and Applications. 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Virtual Event, December 2021 and 2022, Proceedings},
        proceedings_a={BDTA},
        year={2023},
        month={5},
        keywords={Semi-supervised learning Big data Analysis Co-forest Tri-training hand posture classification},
        doi={10.1007/978-3-031-33614-0_10}
    }
    
  • Kailiang Nan
    Shengnan Hu
    Haozhe Luo
    Patricia Wong
    Saeid Pourroostaei Ardakani
    Year: 2023
    A Semi-supervised Learning Application for Hand Posture Classification
    BDTA
    Springer
    DOI: 10.1007/978-3-031-33614-0_10
Kailiang Nan1, Shengnan Hu1, Haozhe Luo1, Patricia Wong1, Saeid Pourroostaei Ardakani1,*
  • 1: School of Computer Science, University of Nottingham Ningbo China
*Contact email: saeid.ardakani@nottingham.edu.cn

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.

Keywords
Semi-supervised learning Big data Analysis Co-forest Tri-training hand posture classification
Published
2023-05-26
Appears in
SpringerLink
http://dx.doi.org/10.1007/978-3-031-33614-0_10
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