Search Results for author: Werner van Ipenburg

Found 4 papers, 1 papers with code

ResGCN: Attention-based Deep Residual Modeling for Anomaly Detection on Attributed Networks

1 code implementation30 Sep 2020 Yulong Pei, Tianjin Huang, Werner van Ipenburg, Mykola Pechenizkiy

Effectively detecting anomalous nodes in attributed networks is crucial for the success of many real-world applications such as fraud and intrusion detection.

Anomaly Detection Intrusion Detection

Case-Based Reasoning for Assisting Domain Experts in Processing Fraud Alerts of Black-Box Machine Learning Models

no code implementations7 Jul 2019 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.

BIG-bench Machine Learning

A Human-Grounded Evaluation of SHAP for Alert Processing

no code implementations7 Jul 2019 Hilde J. P. Weerts, Werner van Ipenburg, Mykola Pechenizkiy

In this paper we present the results of a human-grounded evaluation of SHAP, an explanation method that has been well-received in the XAI and related communities.

BIG-bench Machine Learning Decision Making

Looking Deeper into Deep Learning Model: Attribution-based Explanations of TextCNN

no code implementations8 Nov 2018 Wenting Xiong, Iftitahu Ni'mah, Juan M. G. Huesca, Werner van Ipenburg, Jan Veldsink, Mykola Pechenizkiy

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 classification.

text-classification Text Classification

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