基于广义福利优化的群体鲁棒特征选择
Population-Robust Feature Selection via Generalized Welfare Optimization
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中文总结 AI 辅助
该研究提出PopFS方法，通过多任务稀疏学习缩小候选池并在硬特征集上搜索，实现群体鲁棒特征选择，在多个数据集的预测任务中提升平均与最差群体性能，且可通过调整福利目标优化特定群体表现。
中文摘要 AI 辅助
选择收集哪些特征是一项部署决策：相同的有限问卷、测试面板或传感器集合可能需要服务于多个异质群体。标准特征选择方法通常针对一个大群体进行优化，而现有的鲁棒方法倾向于为每个群体学习一个共享模型。我们引入PopFS，一种学习共享、可部署特征集的方法，该特征集对群体差异具有鲁棒性，同时允许每个群体训练自己的模型。PopFS使用可调的福利目标，让从业者在整体预测收益与对受益最少的群体提供更强保护之间取得平衡。为使该目标在大规模场景下切实可行，PopFS首先使用多任务稀疏学习缩小候选池，然后通过对有前景的新增特征和交换特征进行排序，仅对候选短名单进行完全重新拟合，从而直接在硬特征集上搜索。在来自五个表格和公共卫生数据集的六个预测任务的八组群体划分中，PopFS始终在平均性能和最差群体性能上表现出色，且可扩展至数千个候选特征。一项涵盖43个州的COVID-19 nowcasting（现报预测）研究进一步表明，调整福利目标可在平均性能变化很小的情况下改善服务最差的州，并在所选症状信号上产生可解释的变化。我们的代码可在该https URL获取。
英文摘要
Choosing which features to collect is a deployment decision: the same limited questionnaire, test panel, or sensor set may need to serve several heterogeneous populations. Standard feature-selection methods typically optimize for one large population, while existing robust approaches tend to learn one shared model for every population. We introduce PopFS, a method for learning one shared, deployable feature set that is robust to population differences while letting each pop- ulation train its own model. PopFS uses a tunable welfare objective that lets practitioners balance overall predictive ben- efit against stronger protection of the populations that benefit least. To make this objective practical at scale, PopFS first uses multitask sparse learning to reduce the candidate pool, then searches directly over hard feature sets by ranking promising additions and swaps and fully refitting only a shortlist. Across eight population splits from six prediction tasks drawn from five tabular and public-health datasets, PopFS consistently achieves strong average and worst-population performance while scaling to thousands of candidate features. A 43-state COVID-19 nowcasting study further shows that changing the welfare objective can improve the least-served states with lit- tle change in average performance and yields an interpretable change in the selected symptom signals. Our code is available at https://github.com/Rachel-Lyu/PopFS.
