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In short, our experiments suggest that yes, on average, rotation forest is better than the most common alternatives when all the attributes are real-valued.
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Tree induction for probability-based ranking
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Three myths about dynamic time warping data mining
C. Ratanamahatana and E. Keogh · 2005
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Statistical comparisons of classifiers over multiple data sets
J. Demšar · 2006
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Numerical Optimization
J. Nocedal and S. Wright · 2006
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Rotation forest: A new classifier ensemble method
J. Rodriguez, L. Kuncheva, and C. Alonso · 2006
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An experimental study on rotation forest ensembles
L. Kuncheva and J. Rodriguez · 2007
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Time series classification with ensembles of elastic distance measures
J. Lines and A. Bagnall · 2015
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The BOSS is concerned with time series classification in the presence of noise
P. Schäfer · 2015
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Random rotation ensembles
R. Blaser amd P. Fryzlewicz · 2016
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Should we really use post-hoc tests based on mean-ranks?
A. Benavoli, G. Corani, and F. Mangili · 2016
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XGBoost: A Scalable Tree Boosting System
T. Chen · 2016
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Are random forests truly the best classifiers?
M. Wainberg, B. Alipanahi, and B. Frey · 2016
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S. García and F. Herrera · 2008
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Cancer classification using rotation forest
K. Liu and D. Huang · 2008
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On over-fitting in model selection and subsequent selection bias in performance evaluation
G. Cawley and N. Talbot · 2010
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A practical guide to support vector classification, 2010
C. Hsu, C. Chang, and C. Lin · 2010
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An empirical evaluation of rotation-based ensemble classifiers for customer churn prediction
K. De Bock and D. Van den Poel · 2011
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LIBSVM: A library for support vector machines
C. Chang and C. Lin · 2011
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The great time series classification bake off: a review and experimental evaluation of recent algorithmic advances
A. Bagnall, J. Lines, A. Bostrom, J. Large, and E. Keogh · 2017
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Binary shapelet transform for multiclass time series classification
A. Bostrom and A. Bagnall · 2017
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The UEA multivariate time series classification archive, 2018
A. Bagnall, H. Dau, J. Lines, M. Flynn, J. Large, A. Bostrom, P. Southam, and E. Keogh · 2018
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H. Dau, A. Bagnall, K. Kamgar, M. Yeh, Y. Zhu, S. Gharghabi, and C. Ratanamahatana · 2018
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Optimizing dynamic time warping’s window width for time series data mining applications
H. Dau, D. Silva, F. Petitjean, G. Forestier, A. Bagnall, and E. Keogh · 2018
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Time series classification with HIVE-COTE: The hierarchical vote collective of transformation-based ensembles
J. Lines, S. Taylor, and A. Bagnall · 2018
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Efficient search of the best warping window for dynamic time warping
C. Tan, M. Herrmann, G. Forestierand G. Webb, and F. Petitjean · 2018
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Machine learning automation toolbox (MLaut)
V. Kazakov and F. Kiraly · 2019
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Comparison of 14 different families of classification algorithms on 115 binary datasets
J. Wainer · 2019
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