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An Adaptive Machine Learning System for predicting recurrence of child maltreatment: A routine activity theory perspective
Knowledge-Based Systems ( IF 8.8 ) Pub Date : 2021-06-05 , DOI: 10.1016/j.knosys.2021.107164
Yuzhang Han , Minoo Modaresnezhad , Hamid Nemati

Child maltreatment, including abuse and neglect of children, is one of the most appalling activities routinely occurring in the United States. Every year millions of child maltreatment incidents are reported to Child Protection Services (CPS) agencies. Experts in child protection face enormous workloads of analyzing reported incidents to assess the victims’ risk of experiencing a reoccurrence of maltreatment in the future. However, the existing systems deployed to help the experts are limited in two aspects: first, most have a limited capability in integrating a large amount of data originating from various entities dealing with child maltreatment; second, they are not adaptable enough to accommodate various degrees of un-structuredness inherent in the knowledge-intensive nature of CPS’s tasks, thus relying on CPS and various other experts for system configuration beyond typical users’ skillsets. In response, we propose an adaptive machine learning system (AMLS) inspired by the Routine Activity Theory focused on predicting recurrent child maltreatment. Our system offers a robust prediction for the recurrence of child maltreatment using a multi-faceted adaptive capability. We perform and report the results of extensive computational analysis to demonstrate the superiority of our system’s performance over various existing systems currently deployed.



中文翻译:

预测虐待儿童复发的自适应机器学习系统:常规活动理论视角

虐待儿童,包括虐待和忽视儿童,是美国经常发生的最令人震惊的活动之一。每年有数百万儿童虐待事件被报告给儿童保护服务 (CPS) 机构。儿童保护专家面临着分析报告事件以评估受害者未来再次遭受虐待的风险的巨大工作量。然而,现有的用于帮助专家的系统存在两个方面的局限性:第一,大多数系统在整合来自处理虐待儿童的各个实体的大量数据方面的能力有限;其次,它们的适应性不足以适应 CPS 任务的知识密集型本质所固有的各种程度的非结构化,因此依靠 CPS 和其他各种专家进行系统配置,超出了典型用户的技能范围。作为回应,我们提出了一种受日常活动理论启发的自适应机器学习系统(AMLS),专注于预测复发性儿童虐待。我们的系统使用多方面的适应能力为虐待儿童的复发提供了可靠的预测。我们执行并报告大量计算分析的结果,以证明我们系统的性能优于当前部署的各种现有系统。我们的系统使用多方面的适应能力为虐待儿童的复发提供了可靠的预测。我们执行并报告大量计算分析的结果,以证明我们系统的性能优于当前部署的各种现有系统。我们的系统使用多方面的适应能力为虐待儿童的复发提供了可靠的预测。我们执行并报告大量计算分析的结果,以证明我们系统的性能优于当前部署的各种现有系统。

更新日期:2021-06-09
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