Feature-Weighted Maximum Representative Subsampling

• URL: http://arxiv.org/abs/2603.01013v1
• Date: Sun, 01 Mar 2026 09:47:14 GMT
• Title: Feature-Weighted Maximum Representative Subsampling
• Authors: Tony Hauptmann, Stefan Kramer,
• Abstract summary: We develop an algorithm that uses feature weights to minimize the impact of highly biased features on the computation of sample weights.<n>Our algorithm is based on Maximum Representative Subsampling (MRS), which debiases datasets by aligning a non-representative sample with a representative one.<n>The new algorithm, named feature-weighted MRS, decreases the emphasis on highly biased features, allowing it to retain more instances for downstream tasks.
• Score: 1.5960546024967324
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: In the social sciences, it is often necessary to debias studies and surveys before valid conclusions can be drawn. Debiasing algorithms enable the computational removal of bias using sample weights. However, an issue arises when only a subset of features is highly biased, while the rest is already representative. Algorithms need to strongly alter the sample distribution to manage a few highly biased features, which can in turn introduce bias into already representative variables. To address this issue, we developed a method that uses feature weights to minimize the impact of highly biased features on the computation of sample weights. Our algorithm is based on Maximum Representative Subsampling (MRS), which debiases datasets by aligning a non-representative sample with a representative one through iterative removal of elements to create a representative subsample. The new algorithm, named feature-weighted MRS (FW-MRS), decreases the emphasis on highly biased features, allowing it to retain more instances for downstream tasks. The feature weights are derived from the feature importance of a domain classifier trained to differentiate between the representative and non-representative datasets. We validated FW-MRS using eight tabular datasets, each of which we artificially biased. Biased features can be important for downstream tasks, and focusing less on them could lead to a decline in generalization. For this reason, we assessed the generalization performance of FW-MRS on downstream tasks and found no statistically significant differences. Additionally, FW-MRS was applied to a real-world dataset from the social sciences. The source code is available at https://github.com/kramerlab/FeatureWeightDebiasing.

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