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Contextual Outlier Interpretation
v1v2v3 (latest)

Contextual Outlier Interpretation

28 November 2017
Ninghao Liu
DongHwa Shin
Helen Zhou
ArXiv (abs)PDFHTML

Papers citing "Contextual Outlier Interpretation"

14 / 14 papers shown
Title
Root Cause Analysis of Outliers with Missing Structural Knowledge
Root Cause Analysis of Outliers with Missing Structural Knowledge
Nastaran Okati
Sergio Hernan Garrido Mejia
William Orchard
Patrick Blöbaum
Dominik Janzing
93
2
0
07 Jun 2024
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection
  Model
Using Kernel SHAP XAI Method to optimize the Network Anomaly Detection Model
Khushnaseeb Roshan
Aasim Zafar
82
17
0
31 Jul 2023
Interactive System-wise Anomaly Detection
Interactive System-wise Anomaly Detection
Guanchu Wang
Ninghao Liu
Daochen Zha
Helen Zhou
AAML
41
1
0
21 Apr 2023
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion
  Theory
Cluster Purging: Efficient Outlier Detection based on Rate-Distortion Theory
M. Toller
Bernhard C. Geiger
Roman Kern
41
5
0
22 Feb 2023
A Survey on Explainable Anomaly Detection
A Survey on Explainable Anomaly Detection
Zhong Li
Yuxuan Zhu
M. Leeuwen
115
80
0
13 Oct 2022
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic
  Review on Evaluating Explainable AI
From Anecdotal Evidence to Quantitative Evaluation Methods: A Systematic Review on Evaluating Explainable AI
Meike Nauta
Jan Trienes
Shreyasi Pathak
Elisa Nguyen
Michelle Peters
Yasmin Schmitt
Jorg Schlotterer
M. V. Keulen
C. Seifert
ELMXAI
178
423
0
20 Jan 2022
Utilizing XAI technique to improve autoencoder based model for computer
  network anomaly detection with shapley additive explanation(SHAP)
Utilizing XAI technique to improve autoencoder based model for computer network anomaly detection with shapley additive explanation(SHAP)
Khushnaseeb Roshan
Aasim Zafar
AAML
65
54
0
14 Dec 2021
DeepAID: Interpreting and Improving Deep Learning-based Anomaly
  Detection in Security Applications
DeepAID: Interpreting and Improving Deep Learning-based Anomaly Detection in Security Applications
Dongqi Han
Zhiliang Wang
Wenqi Chen
Ying Zhong
Su Wang
Han Zhang
Jiahai Yang
Xingang Shi
Xia Yin
AAML
67
81
0
23 Sep 2021
Explainable Machine Learning for Fraud Detection
Explainable Machine Learning for Fraud Detection
I. Psychoula
A. Gutmann
Pradip Mainali
Sharon H. Lee
Paul Dunphy
F. Petitcolas
FaML
139
37
0
13 May 2021
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly
  Detection
Shapley Values of Reconstruction Errors of PCA for Explaining Anomaly Detection
Naoya Takeishi
FAtt
151
33
0
08 Sep 2019
Explaining Anomalies Detected by Autoencoders Using SHAP
Explaining Anomalies Detected by Autoencoders Using SHAP
Liat Antwarg
Ronnie Mindlin Miller
Bracha Shapira
Lior Rokach
FAttTDI
77
86
0
06 Mar 2019
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class
  Models
Towards Explaining Anomalies: A Deep Taylor Decomposition of One-Class Models
Jacob R. Kauffmann
K. Müller
G. Montavon
DRL
77
98
0
16 May 2018
Explainable Recommendation: A Survey and New Perspectives
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang
Xu Chen
XAILRM
124
883
0
30 Apr 2018
Towards Explanation of DNN-based Prediction with Guided Feature
  Inversion
Towards Explanation of DNN-based Prediction with Guided Feature Inversion
Mengnan Du
Ninghao Liu
Qingquan Song
Helen Zhou
FAtt
106
127
0
19 Mar 2018
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