ResearchTrend.AI
  • Communities
  • Connect sessions
  • AI calendar
  • Organizations
  • Join Slack
  • Contact Sales
Papers
Communities
Social Events
Terms and Conditions
Pricing
Contact Sales
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2026 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2005.03645
  4. Cited By
XEM: An Explainable-by-Design Ensemble Method for Multivariate Time
  Series Classification
v1v2v3v4v5 (latest)

XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification

7 May 2020
Kevin Fauvel
Elisa Fromont
Véronique Masson
P. Faverdin
Alexandre Termier
    AI4TS
ArXiv (abs)PDFHTML

Papers citing "XEM: An Explainable-by-Design Ensemble Method for Multivariate Time Series Classification"

7 / 7 papers shown
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Evaluation of (Un-)Supervised Machine Learning Methods for GNSS Interference Classification with Real-World Data Discrepancies
Lucas Heublein
Nisha Lakshmana Raichur
Tobias Feigl
Tobias Brieger
Fin Heuer
Lennart Asbach
A. Rügamer
Felix Ott
538
11
0
31 Mar 2025
LCE: An Augmented Combination of Bagging and Boosting in Python
LCE: An Augmented Combination of Bagging and Boosting in Python
Kevin Fauvel
Elisa Fromont
Véronique Masson
P. Faverdin
Alexandre Termier
187
1
0
14 Aug 2023
Fusing Structure from Motion and Simulation-Augmented Pose Regression
  from Optical Flow for Challenging Indoor Environments
Fusing Structure from Motion and Simulation-Augmented Pose Regression from Optical Flow for Challenging Indoor EnvironmentsJournal of Visual Communication and Image Representation (JVCIR), 2023
Felix Ott
Lucas Heublein
David Rügamer
B. Bischl
Christopher Mutschler
544
6
0
14 Apr 2023
Explainable classification of astronomical uncertain time series
Explainable classification of astronomical uncertain time series
Michael Franklin Mbouopda
E. Ishida
E. M. Nguifo
E. Gangler
AI4TS
337
0
0
28 Sep 2022
An Efficient Federated Distillation Learning System for Multi-task Time
  Series Classification
An Efficient Federated Distillation Learning System for Multi-task Time Series ClassificationIEEE Transactions on Instrumentation and Measurement (IEEE Trans. Instrum. Meas.), 2021
Huanlai Xing
Zhiwen Xiao
R. Qu
Zonghai Zhu
Bowen Zhao
FedML
272
126
0
30 Dec 2021
The Semantic Adjacency Criterion in Time Intervals Mining
The Semantic Adjacency Criterion in Time Intervals MiningBig Data and Cognitive Computing (BDCC), 2021
Alexander Shknevsky
Yuval Shahar
Robert Moskovitch
162
3
0
11 Jan 2021
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
Kevin Fauvel
Véronique Masson
Elisa Fromont
AI4TS
351
22
0
29 May 2020
1
Page 1 of 1