A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification-Reference-Cited by-同舟云学术

A novel bias-alleviated hybrid ensemble model based on over-sampling and post-processing for fair classification

Published:2023-03-17 Issue:1 Volume:35 Page:
ISSN:0954-0091
Container-title:Connection Science
language:en
Short-container-title:Connection Science

Author:

He Fang¹,Wu Xiaoxia²,Zhang Wenyu¹,Huang Xiaoling³

Affiliation:

1. School of Information Management and Artificial Intelligence, Zhejiang University of Finance and Economics, Hangzhou, People’s Republic of China

2. Department of Financial Accounting, Zhejiang Institute of Economics and Trade, Hangzhou, People’s Republic of China

3. Library, Zhejiang University of Finance and Economics, Hangzhou, People’s Republic of China

Funder

Fundamental Research Funds for the Provincial Universities of Zhejiang Institute of Economics and Trade

National Natural Science Foundation of China

Zhejiang Natural Science Foundation of China

Zhejiang Key R & D Project of China

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Human-Computer Interaction,Software

Link

https://www.tandfonline.com/doi/pdf/10.1080/09540091.2023.2184310

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3. AI Fairness 360: An extensible toolkit for detecting and mitigating algorithmic bias

4. Bagging predictors

5. The Balanced Accuracy and Its Posterior Distribution

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