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Towards fair machine learning using many-objective feature selection

delete2025-06-17
delete0
PRE
AI
U
Uchechukwu F. Njoku
A
Alberto Abelló
B
Besim Bilalli
G
Gianluca Bontempi
DOI：10.1016/j.asoc.2025.113411delete
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Abstract

Abstract

En 中文
• Introduction of a fair feature selection approach leveraging many-objective optimization. • Extensive experimentation and statistical validation demonstrating the effectiveness of our approach. • Comparison with three baseline methods, including IBM AI Fairness 360. • Identification of optimal termination criteria, specifically the number of generations, for effective convergence through hyperparameter tuning.
Keywords:
Fairness
Feature selection
Dimensionality reduction
Data preprocessing
Search problems
Many-objective optimization

Journal

Applied Soft Computing cover
Applied Soft Computing
IF:
6.6
Papers:
1.4W
Citations:
4.8W

Organization

No organization information available