家庭暴力再犯预测的决策树方法
A Decision Tree Approach to Predicting Recidivism in Domestic Violence
摘要：
家庭暴力是一个高度性别化的全球性社会和公共卫生问题。能够准确预测家庭暴力的再犯率，即，对以前被定罪的犯罪人重新犯罪的调查可以加快和改善警察和一线机构的风险评估程序，更好地保护家庭暴力受害者，并有可能防止家庭暴力今后再次发生。以前的工作在DV累犯采用了不同的分类技术，包括决策树（DT）归纳和逻辑回归，其中的主要重点是实现高的预测精度。因此，即使是经过培训的DT的图表，由于其大小和复杂性，也往往难以解释，这使得决策具有挑战性。鉴于模型准确性和可解释性之间经常存在权衡，在这项工作中，我们的目标是采用DT归纳来获得可解释的树以及高预测准确性。具体来说，我们实现和评估不同的方法来处理类不平衡以及特征选择。与以前采用逻辑回归的DV累犯预测工作相比，我们的方法可以通过仅使用11个可用特征中的3个并生成仅包含4个叶节点的可理解的决策树来实现可比较的ROC曲线下面积结果。
英文摘要：
Domestic violence (DV) is a global social and public health issue that is highly gendered. Being able to accurately predict DV recidivism, i.e., re-offending of a previously convicted offender, can speed up and improve risk assessment procedures for police and front-line agencies, better protect victims of DV, and potentially prevent future re-occurrences of DV. Previous work in DV recidivism has employed different classification techniques, including decision tree (DT) induction and logistic regression, where the main focus was on achieving high prediction accuracy. As a result, even the diagrams of trained DTs were often too difficult to interpret due to their size and complexity, making decision-making challenging. Given there is often a trade-off between model accuracy and interpretability, in this work our aim is to employ DT induction to obtain both interpretable trees as well as high prediction accuracy. Specifically, we implement and evaluate different approaches to deal with class imbalance as well as feature selection. Compared to previous work in DV recidivism prediction that employed logistic regression, our approach can achieve comparable area under the ROC curve results by using only 3 of 11 available features and generating understandable decision trees that contain only 4 leaf nodes.
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