@article{ART003063543},
author={Beom Kwon},
title={Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique},
journal={Journal of The Korea Society of Computer and Information},
issn={1598-849X},
year={2024},
volume={29},
number={3},
pages={55-66},
doi={10.9708/jksci.2024.29.03.055}
TY - JOUR
AU - Beom Kwon
TI - Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique
JO - Journal of The Korea Society of Computer and Information
PY - 2024
VL - 29
IS - 3
PB - The Korean Society Of Computer And Information
SP - 55
EP - 66
SN - 1598-849X
AB - Gender classification techniques have received a lot of attention from researchers because they can be used in various fields such as forensics, surveillance systems, and demographic studies. As previous studies have shown that there are distinctive features between male and female gait, various techniques have been proposed to classify gender from three dimensional(3-D) gait data. However, some of the gait features extracted from 3-D gait data using existing techniques are similar or redundant to each other or do not help in gender classification. In this study, we propose a method to select features that are useful for gender classification using a correlation-based feature selection technique. To demonstrate the effectiveness of the proposed feature selection technique, we compare the performance of gender classification models before and after applying the proposed feature selection technique using a 3-D gait dataset available on the Internet. Eight machine learning algorithms applicable to binary classification problems were utilized in the experiments. The experimental results show that the proposed feature selection technique can reduce the number of features by 22, from 82 to 60, while maintaining the gender classification performance.
KW - Artificial Intelligence;Feature Extraction;Feature Selection;Gait Data;Gender Classification;Machine Learning
DO - 10.9708/jksci.2024.29.03.055
ER -
Beom Kwon. (2024). Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique. Journal of The Korea Society of Computer and Information, 29(3), 55-66.
Beom Kwon. 2024, "Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique", Journal of The Korea Society of Computer and Information, vol.29, no.3 pp.55-66. Available from: doi:10.9708/jksci.2024.29.03.055
Beom Kwon "Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique" Journal of The Korea Society of Computer and Information 29.3 pp.55-66 (2024) : 55.
Beom Kwon. Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique. 2024; 29(3), 55-66. Available from: doi:10.9708/jksci.2024.29.03.055
Beom Kwon. "Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique" Journal of The Korea Society of Computer and Information 29, no.3 (2024) : 55-66.doi: 10.9708/jksci.2024.29.03.055
Beom Kwon. Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique. Journal of The Korea Society of Computer and Information, 29(3), 55-66. doi: 10.9708/jksci.2024.29.03.055
Beom Kwon. Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique. Journal of The Korea Society of Computer and Information. 2024; 29(3) 55-66. doi: 10.9708/jksci.2024.29.03.055
Beom Kwon. Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique. 2024; 29(3), 55-66. Available from: doi:10.9708/jksci.2024.29.03.055
Beom Kwon. "Gait-Based Gender Classification Using a Correlation-Based Feature Selection Technique" Journal of The Korea Society of Computer and Information 29, no.3 (2024) : 55-66.doi: 10.9708/jksci.2024.29.03.055
