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A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers

Chen, Zhenpeng; Zhang, Jie M; Sarro, Federica; Harman, Mark; (2023) A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers. ACM Transactions on Software Engineering and Methodology (In press). Green open access

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Type: Article
Title: A Comprehensive Empirical Study of Bias Mitigation Methods for Machine Learning Classifiers
Open access status: An open access version is available from UCL Discovery
Publisher version: https://arxiv.org/abs/2207.03277
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
UCL classification: UCL
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10163249
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