用表示学习提升流浪者服务分配预测准确率
Predictive Modeling of Homeless Service Assignment: A Representation Learning Approach
- 从历史数据中学习服务间时序与功能关系
- 挖掘个体间隐含关联以生成增强特征
- 适用于需优化社会服务分配的决策系统
近年来，利用机器学习进行流浪者服务分配受到越来越多关注。然而，行政数据中个体特征的分类性质限制了此类任务的机器学习方法精度。本文认为，从这些特征中提取潜在表示，并同时利用实例间的内在关联，对于算法改进现有分配决策过程至关重要。所提方法从历史数据中学习服务之间的时序与功能关系，以及个体间未被观测但相关的潜在联系，从而生成显著提升下一阶段服务分配预测性能的特征，优于当前最先进方法。
原文摘要 · Abstract (English)
In recent years, there has been growing interest in leveraging machine learning for homeless service assignment. However, the categorical nature of administrative data recorded for homeless individuals hinders the development of accurate machine learning methods for this task. This work asserts that deriving latent representations of such features, while at the same time leveraging underlying relationships between instances is crucial in algorithmically enhancing the existing assignment decision-making process. Our proposed approach learns temporal and functional relationships between services from historical data, as well as unobserved but relevant relationships between individuals to generate features that significantly improve the prediction of the next service assignment compared to the state-of-the-art.
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