Related references
Note: Only part of the references are listed.The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
Anthony Bagnall et al.
DATA MINING AND KNOWLEDGE DISCOVERY (2017)
Time-Series Classification with COTE: The Collective of Transformation-Based Ensembles
Anthony Bagnall et al.
IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING (2015)
A weighted voting framework for classifiers ensembles
Ludmila I. Kuncheva et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2014)
An analysis of diversity measures
E. K. Tang et al.
MACHINE LEARNING (2006)
Rotation forest:: A new classifier ensemble method
Juan J. Rodriguez et al.
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2006)
Extremely randomized trees
P Geurts et al.
MACHINE LEARNING (2006)
Is combining classifiers with stacking better than selecting the best one?
S Dzeroski et al.
MACHINE LEARNING (2004)
Tree induction for probability-based ranking
F Provost et al.
MACHINE LEARNING (2003)
Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
LI Kuncheva et al.
MACHINE LEARNING (2003)
Greedy function approximation: A gradient boosting machine
JH Friedman
ANNALS OF STATISTICS (2001)
An approach to the automatic design of multiple classifier systems
G Giacinto et al.
PATTERN RECOGNITION LETTERS (2001)
MultiBoosting: A technique for combining boosting and wagging
GI Webb
MACHINE LEARNING (2000)
An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization
TG Dietterich
MACHINE LEARNING (2000)