TY - GEN
T1 - A Semi-supervised Learning Application for Hand Posture Classification
AU - Nan, Kailiang
AU - Hu, Shengnan
AU - Luo, Haozhe
AU - Wong, Patricia
AU - Pourroostaei Ardakani, Saeid
N1 - Publisher Copyright:
© 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Big data Analysis
KW - Co-forest
KW - Semi-supervised learning
KW - Tri-training
KW - hand posture classification
UR - http://www.scopus.com/inward/record.url?scp=85163382438&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-33614-0_10
DO - 10.1007/978-3-031-33614-0_10
M3 - Conference contribution
AN - SCOPUS:85163382438
SN - 9783031336133
T3 - Lecture Notes of the Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering, LNICST
SP - 137
EP - 148
BT - Big Data Technologies and Applications - 11th and 12th EAI International Conference, BDTA 2021 and BDTA 2022, Proceedings
A2 - Hou, Rui
A2 - Huang, Huan
A2 - Zeng, Deze
A2 - Xia, Guisong
A2 - A. Ghany, Kareem Kamal
A2 - Zawbaa, Hossam M.
PB - Springer Science and Business Media Deutschland GmbH
T2 - 11th and 12th EAI International Conference on Big Data Technologies and Applications, BDTA 2021 and BDTA 2022
Y2 - 10 December 2022 through 11 December 2022
ER -