A Semi-supervised Learning Application for Hand Posture Classification

Kailiang Nan, Shengnan Hu, Haozhe Luo, Patricia Wong, Saeid Pourroostaei Ardakani

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review


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.

Original languageEnglish
Title of host publicationBig Data Technologies and Applications - 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Proceedings
EditorsRui Hou, Huan Huang, Deze Zeng, Guisong Xia, Kareem Kamal A. Ghany, Hossam M. Zawbaa
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages12
ISBN (Print)9783031336133
Publication statusPublished - 2023
Event11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022 - Virtual, Online
Duration: 10 Dec 202211 Dec 2022

Publication series

NameLecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
Volume480 LNICST
ISSN (Print)1867-8211
ISSN (Electronic)1867-822X


Conference11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022
CityVirtual, Online


  • Big data Analysis
  • Co-forest
  • Semi-supervised learning
  • Tri-training
  • hand posture classification

ASJC Scopus subject areas

  • Computer Networks and Communications


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