  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%.
