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Should we really use post-hoc tests based on mean-ranks?

Should we really use post-hoc tests based on mean-ranks?

9 May 2015
A. Benavoli
Giorgio Corani
Francesca Mangili
ArXiv (abs)PDFHTML

Papers citing "Should we really use post-hoc tests based on mean-ranks?"

25 / 125 papers shown
Title
Methods and open-source toolkit for analyzing and visualizing challenge
  results
Methods and open-source toolkit for analyzing and visualizing challenge results
Manuel Wiesenfarth
Annika Reinke
Bennett A. Landman
M. Jorge Cardoso
Lena Maier-Hein
A. Kopp-Schneider
66
98
0
11 Oct 2019
Investigating the Effectiveness of Representations Based on
  Word-Embeddings in Active Learning for Labelling Text Datasets
Investigating the Effectiveness of Representations Based on Word-Embeddings in Active Learning for Labelling Text Datasets
Jinghui Lu
M. Henchion
Brian Mac Namee
AI4TS
39
9
0
04 Oct 2019
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning
Weighted Sampling for Combined Model Selection and Hyperparameter Tuning
Dimitrios Sarigiannis
Thomas Parnell
Haris Pozidis
359
3
0
16 Sep 2019
GENDIS: GENetic DIscovery of Shapelets
GENDIS: GENetic DIscovery of Shapelets
Gilles Vandewiele
F. Ongenae
F. Turck
AI4TS
60
12
0
13 Sep 2019
InceptionTime: Finding AlexNet for Time Series Classification
InceptionTime: Finding AlexNet for Time Series Classification
Hassan Ismail Fawaz
Benjamin Lucas
Germain Forestier
Charlotte Pelletier
Daniel F. Schmidt
J. Weber
Geoffrey I. Webb
L. Idoumghar
Pierre-Alain Muller
Franccois Petitjean
AI4TS
196
1,134
0
11 Sep 2019
Scalable Dictionary Classifiers for Time Series Classification
Scalable Dictionary Classifiers for Time Series Classification
Matthew Middlehurst
William D. Vickers
A. Bagnall
65
62
0
26 Jul 2019
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series
  Classification
TS-CHIEF: A Scalable and Accurate Forest Algorithm for Time Series Classification
Ahmed Shifaz
Charlotte Pelletier
F. Petitjean
Geoffrey I. Webb
AI4TS
102
186
0
25 Jun 2019
Ordinal Regression as Structured Classification
Ordinal Regression as Structured Classification
Niall Twomey
Rafael Poyiadzi
Callum Mann
Raúl Santos-Rodríguez
CML
19
0
0
31 May 2019
Synthetic Oversampling of Multi-Label Data based on Local Label
  Distribution
Synthetic Oversampling of Multi-Label Data based on Local Label Distribution
B. Liu
Grigorios Tsoumakas
43
26
0
02 May 2019
Deep Neural Network Ensembles for Time Series Classification
Deep Neural Network Ensembles for Time Series Classification
Hassan Ismail Fawaz
Germain Forestier
J. Weber
L. Idoumghar
Pierre-Alain Muller
AI4TSUQCV
63
71
0
15 Mar 2019
Deep Reinforcement Learning for Imbalanced Classification
Deep Reinforcement Learning for Imbalanced Classification
Enlu Lin
Qiong Chen
Xiaoming Qi
OffRL
59
183
0
05 Jan 2019
Can automated smoothing significantly improve benchmark time series
  classification algorithms?
Can automated smoothing significantly improve benchmark time series classification algorithms?
J. Large
Paul Southam
A. Bagnall
AI4TS
16
3
0
01 Nov 2018
The UCR Time Series Archive
The UCR Time Series Archive
Hoang Anh Dau
A. Bagnall
Kaveh Kamgar
Chin-Chia Michael Yeh
Yan Zhu
Shaghayegh Gharghabi
C. Ratanamahatana
Eamonn Keogh
70
838
0
17 Oct 2018
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic
  Ensemble Selection
FIRE-DES++: Enhanced Online Pruning of Base Classifiers for Dynamic Ensemble Selection
Rafael M. O. Cruz
Dayvid V. R. Oliveira
George D. C. Cavalcanti
R. Sabourin
36
54
0
01 Oct 2018
Is rotation forest the best classifier for problems with continuous
  features?
Is rotation forest the best classifier for problems with continuous features?
A. Bagnall
Michael Flynn
J. Large
Jason Lines
A. Bostrom
G. Cawley
64
39
0
18 Sep 2018
Short-term Cognitive Networks, Flexible Reasoning and Nonsynaptic
  Learning
Short-term Cognitive Networks, Flexible Reasoning and Nonsynaptic Learning
Gonzalo Nápoles
Frank Vanhoenshoven
K. Vanhoof
11
19
0
16 Sep 2018
Deep learning for time series classification: a review
Deep learning for time series classification: a review
Hassan Ismail Fawaz
Germain Forestier
J. Weber
L. Idoumghar
Pierre-Alain Muller
AI4TSAI4CE
376
2,727
0
12 Sep 2018
Online local pool generation for dynamic classifier selection: an
  extended version
Online local pool generation for dynamic classifier selection: an extended version
Mariana A. Souza
George D. C. Cavalcanti
Rafael M. O. Cruz
R. Sabourin
44
26
0
05 Sep 2018
Making Classifier Chains Resilient to Class Imbalance
Making Classifier Chains Resilient to Class Imbalance
Bin Liu
Grigorios Tsoumakas
42
17
0
30 Jul 2018
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
K-Nearest Oracles Borderline Dynamic Classifier Ensemble Selection
Dayvid V. R. Oliveira
George D. C. Cavalcanti
Thyago N. Porpino
Rafael M. O. Cruz
R. Sabourin
35
11
0
18 Apr 2018
QCBA: Improving Rule Classifiers Learned from Quantitative Data by
  Recovering Information Lost by Discretisation
QCBA: Improving Rule Classifiers Learned from Quantitative Data by Recovering Information Lost by Discretisation
Tomáš Kliegr
E. Izquierdo
27
5
0
28 Nov 2017
The Heterogeneous Ensembles of Standard Classification Algorithms
  (HESCA): the Whole is Greater than the Sum of its Parts
The Heterogeneous Ensembles of Standard Classification Algorithms (HESCA): the Whole is Greater than the Sum of its Parts
J. Large
Jason Lines
A. Bagnall
53
28
0
25 Oct 2017
Simulated Data Experiments for Time Series Classification Part 1:
  Accuracy Comparison with Default Settings
Simulated Data Experiments for Time Series Classification Part 1: Accuracy Comparison with Default Settings
A. Bagnall
A. Bostrom
J. Large
Jason Lines
58
14
0
28 Mar 2017
On the Use of Default Parameter Settings in the Empirical Evaluation of
  Classification Algorithms
On the Use of Default Parameter Settings in the Empirical Evaluation of Classification Algorithms
A. Bagnall
G. Cawley
48
20
0
20 Mar 2017
Time for a change: a tutorial for comparing multiple classifiers through
  Bayesian analysis
Time for a change: a tutorial for comparing multiple classifiers through Bayesian analysis
A. Benavoli
Giorgio Corani
J. Demšar
Marco Zaffalon
BDL
117
426
0
14 Jun 2016
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