Fair Oversampling Technique using Heterogeneous Clusters
Ryosuke Sonoda
Class imbalance and group (e.g., race, gender, and age) imbalance are
acknowledged as two reasons in data that hinder the trade-off between fairness
and utility of machine learning classifiers. Existing techniques have jointly
addressed issues regarding class imbalance and group imbalance by proposing
fair over-sampling techniques. Unlike the common oversampling techniques, which
only address class imbalance, fair oversampling techniques significantly
improve the abovementioned trade-off, as they can also address group imbalance.
However, if the size of the original clusters is too small, these techniques
may cause classifier overfitting. To address this problem, we herein develop a
fair oversampling technique using data from heterogeneous clusters. The
proposed technique generates synthetic data that have class-mix features or
group-mix features to make classifiers robust to overfitting. Moreover, we
develop an interpolation method that can enhance the validity of generated
synthetic data by considering the original cluster distribution and data noise.
Finally, we conduct experiments on five realistic datasets and three
classifiers, and the experimental results demonstrate the effectiveness of the
proposed technique in terms of fairness and utility.