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A Performance-Explainability Framework to Benchmark Machine Learning
  Methods: Application to Multivariate Time Series Classifiers

A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers

29 May 2020
Kevin Fauvel
Véronique Masson
Elisa Fromont
    AI4TS
ArXivPDFHTML

Papers citing "A Performance-Explainability Framework to Benchmark Machine Learning Methods: Application to Multivariate Time Series Classifiers"

9 / 9 papers shown
Title
When Bioprocess Engineering Meets Machine Learning: A Survey from the
  Perspective of Automated Bioprocess Development
When Bioprocess Engineering Meets Machine Learning: A Survey from the Perspective of Automated Bioprocess Development
Nghia Duong-Trung
Stefan Born
Jong Woo Kim
M. Schermeyer
Katharina Paulick
...
Thorben Werner
Randolf Scholz
Lars Schmidt-Thieme
Peter Neubauer
Ernesto Martinez
24
20
0
02 Sep 2022
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural
  Network for Internet Traffic Classification
A Lightweight, Efficient and Explainable-by-Design Convolutional Neural Network for Internet Traffic Classification
Kevin Fauvel
Fuxing Chen
Dario Rossi
22
25
0
11 Feb 2022
Random Dilated Shapelet Transform: A New Approach for Time Series
  Shapelets
Random Dilated Shapelet Transform: A New Approach for Time Series Shapelets
Antoine Guillaume
Christel Vrain
Wael Elloumi
AI4TS
40
31
0
28 Sep 2021
Synthetic Benchmarks for Scientific Research in Explainable Machine
  Learning
Synthetic Benchmarks for Scientific Research in Explainable Machine Learning
Yang Liu
Sujay Khandagale
Colin White
W. Neiswanger
31
65
0
23 Jun 2021
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of
  GNN Explanation Methods
Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods
Chirag Agarwal
Marinka Zitnik
Himabindu Lakkaraju
19
51
0
16 Jun 2021
Instance-based Counterfactual Explanations for Time Series
  Classification
Instance-based Counterfactual Explanations for Time Series Classification
Eoin Delaney
Derek Greene
Mark T. Keane
CML
AI4TS
14
89
0
28 Sep 2020
XCM: An Explainable Convolutional Neural Network for Multivariate Time
  Series Classification
XCM: An Explainable Convolutional Neural Network for Multivariate Time Series Classification
Kevin Fauvel
Tao R. Lin
Véronique Masson
Elisa Fromont
Alexandre Termier
BDL
AI4TS
4
100
0
10 Sep 2020
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time
  Series Classification
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification
Kevin Fauvel
Elisa Fromont
Véronique Masson
P. Faverdin
Alexandre Termier
AI4TS
17
41
0
07 May 2020
Towards A Rigorous Science of Interpretable Machine Learning
Towards A Rigorous Science of Interpretable Machine Learning
Finale Doshi-Velez
Been Kim
XAI
FaML
251
3,683
0
28 Feb 2017
1