Multi-characteristic Subject Selection from Biased Datasets

TL;DR

This paper introduces a constrained optimization method for selecting diverse subjects from biased datasets, improving sampling accuracy in experimental studies involving human participants.

Contribution

It proposes a novel optimization-based approach for multi-characteristic subject selection that effectively mitigates bias in datasets.

Findings

Outperforms baseline methods by up to 90% in various experiments

Effective in selecting diverse subjects from biased datasets

Applicable to real-world experimental settings

Abstract

Subject selection plays a critical role in experimental studies, especially ones with human subjects. Anecdotal evidence suggests that many such studies, done at or near university campus settings suffer from selection bias, i.e., the too-many-college-kids-as-subjects problem. Unfortunately, traditional sampling techniques, when applied over biased data, will typically return biased results. In this paper, we tackle the problem of multi-characteristic subject selection from biased datasets. We present a constrained optimization-based method that finds the best possible sampling fractions for the different population subgroups, based on the desired sampling fractions provided by the researcher running the subject selection.We perform an extensive experimental study, using a variety of real datasets. Our results show that our proposed method outperforms the baselines for all problem variations by up to 90%.