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Contextual Outlier Interpretation

Contextual Outlier Interpretation

28 November 2017
Ninghao Liu
DongHwa Shin
Xia Hu
ArXivPDFHTML

Papers citing "Contextual Outlier Interpretation"

13 / 13 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
28
2
0
07 Jun 2024
Outlier detection using flexible categorisation and interrogative
  agendas
Outlier detection using flexible categorisation and interrogative agendas
Marcel Boersma
Krishna Manoorkar
A. Palmigiano
Mattia Panettiere
Apostolos Tzimoulis
N. Wijnberg
37
2
0
19 Dec 2023
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
17
16
0
31 Jul 2023
Interactive System-wise Anomaly Detection
Interactive System-wise Anomaly Detection
Guanchu Wang
Ninghao Liu
Daochen Zha
Xia Hu
AAML
22
1
0
21 Apr 2023
Explainable Contextual Anomaly Detection using Quantile Regression
  Forests
Explainable Contextual Anomaly Detection using Quantile Regression Forests
Zhong Li
M. Leeuwen
AI4TS
49
15
0
22 Feb 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
22
3
0
22 Feb 2023
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits
  by enhancing SHapley Additive exPlanations
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
Ricardo Müller
Marco Schreyer
Timur Sattarov
Damian Borth
AAML
MLAU
35
7
0
19 Sep 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
19
50
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
24
76
0
23 Sep 2021
Explaining Anomalies Detected by Autoencoders Using SHAP
Explaining Anomalies Detected by Autoencoders Using SHAP
Liat Antwarg
Ronnie Mindlin Miller
Bracha Shapira
Lior Rokach
FAtt
TDI
19
86
0
06 Mar 2019
Unsupervised Detection and Explanation of Latent-class Contextual
  Anomalies
Unsupervised Detection and Explanation of Latent-class Contextual Anomalies
Jacob R. Kauffmann
G. Montavon
L. A. Lima
Shinichi Nakajima
K. Müller
Nico Görnitz
16
0
0
29 Jun 2018
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
42
96
0
16 May 2018
Explainable Recommendation: A Survey and New Perspectives
Explainable Recommendation: A Survey and New Perspectives
Yongfeng Zhang
Xu Chen
XAI
LRM
52
866
0
30 Apr 2018
1