论文标题
框架算法求程用于异常检测
Framing Algorithmic Recourse for Anomaly Detection
论文作者
论文摘要
已经探索了有监督的机器学习模型的算法追索问题的问题，以提供决策支持系统中更容易解释，透明和鲁棒的结果。未开发的区域是用于异常检测的算法求程，特别是仅具有离散特征值的表格数据。这里的问题是提出一组反事实，通过基本的异常检测模型认为是正常的，以便应用程序可以将此信息用于解释目的或推荐对策。我们提出了一种方法 - 在表格数据（CARAT）中保留异常算法的背景，该方法是有效，可扩展性且不可知的，对基本的异常检测模型。 Carat使用基于变压器的编码器模型来通过查找可能性很小的特征来解释异常。随后使用异常实例中特征的整体上下文来修改突出显示的功能，从而生成语义相干的反事实。广泛的实验有助于证明克拉的功效。
The problem of algorithmic recourse has been explored for supervised machine learning models, to provide more interpretable, transparent and robust outcomes from decision support systems. An unexplored area is that of algorithmic recourse for anomaly detection, specifically for tabular data with only discrete feature values. Here the problem is to present a set of counterfactuals that are deemed normal by the underlying anomaly detection model so that applications can utilize this information for explanation purposes or to recommend countermeasures. We present an approach -- Context preserving Algorithmic Recourse for Anomalies in Tabular data (CARAT), that is effective, scalable, and agnostic to the underlying anomaly detection model. CARAT uses a transformer based encoder-decoder model to explain an anomaly by finding features with low likelihood. Subsequently semantically coherent counterfactuals are generated by modifying the highlighted features, using the overall context of features in the anomalous instance(s). Extensive experiments help demonstrate the efficacy of CARAT.
