1 code implementation • 30 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.
no code implementations • 7 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.
no code implementations • 7 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.
no code implementations • 8 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.