TY - GEN
T1 - A Case Study of Accurate and Fair Classification
AU - Liu, Xiaoqian
AU - Chao, Zemin
N1 - Publisher Copyright:
© 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
PY - 2024
Y1 - 2024
N2 - As artificial intelligence and human life become increasingly inseparable, the legal or ethical issues faced by artificial intelligence systems in autonomous decision-making are also increasing. During the training process, the algorithms may be influenced by human biases such as gender, race, and other factors, leading to discrimination and affecting fairness. To establish secure intelligent systems, fair machine learning has become a popular research direction. This work demonstrates the existing definitions of fairness and designs experiments to show that combining clustering algorithms into the data handling process can effectively improve the classification accuracy and fairness on bank loan dataset. In the case study, K-means clustering, hierarchical clustering and Gaussian Mixture Model are used, proving that the clustering algorithms can significantly improve the accuracy of the model and ensure the relative fairness of the classification results.
AB - As artificial intelligence and human life become increasingly inseparable, the legal or ethical issues faced by artificial intelligence systems in autonomous decision-making are also increasing. During the training process, the algorithms may be influenced by human biases such as gender, race, and other factors, leading to discrimination and affecting fairness. To establish secure intelligent systems, fair machine learning has become a popular research direction. This work demonstrates the existing definitions of fairness and designs experiments to show that combining clustering algorithms into the data handling process can effectively improve the classification accuracy and fairness on bank loan dataset. In the case study, K-means clustering, hierarchical clustering and Gaussian Mixture Model are used, proving that the clustering algorithms can significantly improve the accuracy of the model and ensure the relative fairness of the classification results.
KW - Clustering algorithm
KW - Fairness
KW - Machine learning
UR - https://www.scopus.com/pages/publications/85186708876
U2 - 10.1007/978-981-99-9243-0_40
DO - 10.1007/978-981-99-9243-0_40
M3 - 会议稿件
AN - SCOPUS:85186708876
SN - 9789819992423
T3 - Lecture Notes in Electrical Engineering
SP - 409
EP - 419
BT - Proceedings of the 13th International Conference on Computer Engineering and Networks - Volume II
A2 - Zhang, Yonghong
A2 - Qi, Lianyong
A2 - Liu, Qi
A2 - Yin, Guangqiang
A2 - Liu, Xiaodong
PB - Springer Science and Business Media Deutschland GmbH
T2 - 13th International Conference on Computer Engineering and Networks, CENet 2023
Y2 - 3 November 2023 through 5 November 2023
ER -