20 citations · 32 across the 3 of their papers we have counts for
4 papers
ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks
Yulong Pei, Tianjin Huang, Werner van Ipenburg +1
Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection. Existing approaches h…
Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models
Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy
In many contexts, it can be useful for domain experts to understand to what extent predictions made by a machine learning model can be trusted. In particular, estimates of trustwor…
A Human-Grounded Evaluation of SHAP for Alert Processing
Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy
In the past years, many new explanation methods have been proposed to achieve interpretability of machine learning predictions. However, the utility of these methods in practical a…
Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN
Wenting Xiong, Iftitahu Ni'mah, Juan M. G. Huesca +3
Layer-wise Relevance Propagation (LRP) and saliency maps have been recently used to explain the predictions of Deep Learning models, specifically in the domain of text classificati…