Data Balancing for Mitigating Sampling Bias in Machine Learning
Ronaldo Lopes Inocêncio Júnior, Márcio P. Basgalupp, Teresa B. Ludermir, Ana Carolina Lorena · 2025
We increasingly integrate technology into our daily activities, and using Machine Learning (ML) algorithms in various domains has become a common practice. However, in crucial sectors where algorithmic decisions significantly impact people's lives, there is a need to scrutinize these decisions more carefully. Using these algorithms in critical areas, such as courtrooms, raises concerns about potential bias and prejudice, directly affecting the justice and partiality of these tools. There is an urge to create algorithms supporting ethical decisions. This paper proposes using data balancing techniques to mitigate the sample bias present in datasets, aiming to make subsequent ML algorithm training more impartial. A version of the ADASYN algorithm is developed, which performs data balancing at both the class level and at the level of protected attributes, enhancing the diversity and representativeness of the protected groups in the datasets. Experimental results show the technique can promote greater fairness in the predictions of different ML models while keeping a good trade-off with overall accuracy.