ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 1809.06705
  4. Cited By
Is rotation forest the best classifier for problems with continuous
  features?
v1v2v3 (latest)

Is rotation forest the best classifier for problems with continuous features?

18 September 2018
A. Bagnall
Michael Flynn
J. Large
Jason Lines
A. Bostrom
G. Cawley
ArXiv (abs)PDFHTML

Papers citing "Is rotation forest the best classifier for problems with continuous features?"

8 / 8 papers shown
Title
Even Faster Hyperbolic Random Forests: A Beltrami-Klein Wrapper Approach
Philippe Chlenski
I. Pe’er
34
1
0
04 Jun 2025
Mixed-Curvature Decision Trees and Random Forests
Mixed-Curvature Decision Trees and Random Forests
Philippe Chlenski
Quentin Chu
I. Pe’er
95
2
0
07 Jun 2024
Unsupervised Feature Based Algorithms for Time Series Extrinsic
  Regression
Unsupervised Feature Based Algorithms for Time Series Extrinsic Regression
David Guijo Rubio
Matthew Middlehurst
Guilherme G. Arcencio
Diego Furtado Silva
A. Bagnall
AI4TS
93
9
0
02 May 2023
Dimension Reduction and MARS
Dimension Reduction and MARS
Yu Liu
Degui Li
Yingcun Xia
80
1
0
11 Feb 2023
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series
  Classifier
The FreshPRINCE: A Simple Transformation Based Pipeline Time Series Classifier
Matthew Middlehurst
A. Bagnall
AI4TS
77
18
0
28 Jan 2022
A tale of two toolkits, report the third: on the usage and performance
  of HIVE-COTE v1.0
A tale of two toolkits, report the third: on the usage and performance of HIVE-COTE v1.0
A. Bagnall
Michael Flynn
J. Large
Jason Lines
Matthew Middlehurst
91
43
0
13 Apr 2020
A tale of two toolkits, report the first: benchmarking time series
  classification algorithms for correctness and efficiency
A tale of two toolkits, report the first: benchmarking time series classification algorithms for correctness and efficiency
A. Bagnall
Franz J. Király
M. Löning
Matthew Middlehurst
George Oastler
AI4TS
73
6
0
12 Sep 2019
Nested cross-validation when selecting classifiers is overzealous for
  most practical applications
Nested cross-validation when selecting classifiers is overzealous for most practical applications
Jacques Wainer
G. Cawley
52
216
0
25 Sep 2018
1