Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion

11 September 2020

Papers citing "Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion"

9 / 9 papers shown

Title
Pareto Data Framework: Steps Towards Resource-Efficient Decision Making Using Minimum Viable Data (MVD) Tashfain Ahmed Josh Siegel 59 1 0 18 Sep 2024
Rethinking Unsupervised Outlier Detection via Multiple Thresholding Zhonghang Liu Panzhong Lu Guoyang Xie Zhichao Lu Wen-Yan Lin 84 0 0 07 Jul 2024
Causal Feature Selection for Responsible Machine Learning Raha Moraffah Paras Sheth Saketh Vishnubhatla Huan Liu CML 60 2 0 05 Feb 2024
McUDI: Model-Centric Unsupervised Degradation Indicator for Failure Prediction AIOps Solutions Lorena Poenaru-Olaru Luís Cruz Jan S. Rellermeyer A. V. Deursen 92 0 0 25 Jan 2024
What's meant by explainable model: A Scoping Review Mallika Mainali Rosina O. Weber XAI 61 0 0 18 Jul 2023
Reconnoitering the class distinguishing abilities of the features, to know them better Payel Sadhukhan S. Palit Kausik Sengupta 55 0 0 23 Nov 2022
Estimating the Contamination Factor's Distribution in Unsupervised Anomaly Detection Lorenzo Perini Paul-Christian Buerkner Arto Klami 74 16 0 19 Oct 2022
EFI: A Toolbox for Feature Importance Fusion and Interpretation in Python Aayush Kumar J. M. Mase D. Rengasamy Benjamin Rothwell Mercedes Torres Torres David A. Winkler Grazziela Figueredo 67 2 0 08 Aug 2022
Mechanistic Interpretation of Machine Learning Inference: A Fuzzy Feature Importance Fusion Approach D. Rengasamy J. M. Mase Mercedes Torres Torres Benjamin Rothwell David A. Winkler Grazziela Figueredo FAtt 36 2 0 22 Oct 2021