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2009.05501
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Towards a More Reliable Interpretation of Machine Learning Outputs for Safety-Critical Systems using Feature Importance Fusion
11 September 2020
D. Rengasamy
Benjamin Rothwell
Grazziela Figueredo
FaML
FAtt
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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
1