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We present a theory of ensemble diversity, explaining the nature of diversity for a wide range of supervised learning scenarios.
The relaxation method of finding the common point of convex sets and its application to the solution of problems in convex programming
Lev M Bregman · 1967
Earlier work this paper cites.
Neural network ensembles
Lars K. Hansen and Peter Salamon · 1990
Earlier work this paper cites.
Neural networks and the bias/variance dilemma
Stuart Geman, Elie Bienenstock, and René Doursat · 1992
Earlier work this paper cites.
When networks disagree: Ensemble methods for hybrid neural networks
Michael P Perrone and Leon N Cooper · 1992
Earlier work this paper cites.
Neural network ensembles, cross validation and active learning
Anders Krogh and Jesper Vedelsby · 1994
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
Bias plus variance decomposition for zero-one loss functions
Ron Kohavi, David H Wolpert, et al · 1996
Earlier work this paper cites.
Generating accurate and diverse members of a neural-network ensemble
David W Opitz and Jude W Shavlik · 1996
Earlier work this paper cites.
Bias, variance and prediction error for classification rules
Robert Tibshirani · 1996
Earlier work this paper cites.
Error correlation and error reduction in ensemble classifiers
Kagan Tumer and Joydeep Ghosh · 1996
Earlier work this paper cites.
Generalization error of ensemble estimators
Naonori Ueda and Ryohei Nakano · 1996
Earlier work this paper cites.
Generalizations of the bias/variance decomposition for prediction error
Gareth James and Trevor Hastie · 1997
Earlier work this paper cites.
Application of majority voting to pattern recognition: an analysis of its behavior and performance
Louisa Lam and SY Suen · 1997
Earlier work this paper cites.
Comparison between product and mean classifier combination rules
David MJ Tax, Robert PW Duin, and Martijn Van Breukelen · 1997
Earlier work this paper cites.
Bias/variance decompositions for likelihood-based estimators
Tom Heskes · 1998
Earlier work this paper cites.
Boosting the margin: A new explanation for the effectiveness of voting methods
Robert Schapire, Yoav Freund, Peter Bartlett, and Wee Sun Lee · 1998
Earlier work this paper cites.
Ensemble learning via negative correlation
Yong Liu and Xin Yao · 1999
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Ensemble methods in machine learning
Thomas G Dietterich · 2000
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A unified bias-variance decomposition
Pedro Domingos · 2000
Earlier work this paper cites.
Random forests
Leo Breiman · 2001
Earlier work this paper cites.
Rapid object detection using a boosted cascade of simple features
Paul Viola and Michael Jones · 2001
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Contributions to decision tree induction: bias/variance tradeoff and time series classification
Pierre Geurts · 2002
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That elusive diversity in classifier ensembles
Ludmila Kuncheva · 2003
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Measures of diversity in classifier ensembles and their relationship with the ensemble accuracy
Ludmila Kuncheva and Christopher Whitaker · 2003
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Clustering with bregman divergences
Arindam Banerjee, Srujana Merugu, Inderjit S Dhillon, and Joydeep Ghosh · 2005
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Diversity creation methods: a survey and categorisation
Gavin Brown, Jeremy Wyatt, Rachel Harris, and Xin Yao · 2005
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An ensemble diversity approach to supervised binary hashing
Miguel A Carreira-Perpinán and Ramin Raziperchikolaei · 2016
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Deep Learning
Ian Goodfellow, Yoshua Bengio, and Aaron Courville · 2016
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Generalized ambiguity decompositions for classification with applications in active learning and unsupervised ensemble pruning
Zhengshen Jiang, Hongzhi Liu, Bin Fu, and Zhonghai Wu · 2017
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Simple and scalable predictive uncertainty estimation using deep ensembles
Balaji Lakshminarayanan, Alexander Pritzel, and Charles Blundell · 2017
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Reconciling modern machine learning and the bias-variance trade-off
Mikhail Belkin, Daniel Hsu, Siyuan Ma, and Soumik Mandal · 2018
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Managing diversity in regression ensembles
Gavin Brown, Jeremy L Wyatt, and Peter Tiňo · 2006
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Evidence contrary to the statistical view of boosting
David Mease and Abraham Wyner · 2008
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An information theoretic perspective on multiple classifier systems
Gavin Brown · 2009
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Sided and symmetrized bregman centroids
Frank Nielsen and Richard Nock · 2009
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“good” and “bad” diversity in majority vote ensembles
Gavin Brown and Ludmila Kuncheva · 2010
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Multi-information ensemble diversity
Zhi-Hua Zhou and Nan Li · 2010
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Yijun Bian and Huanhuan Chen · 2019
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A novel diversity measure and classifier selection approach for generating ensemble classifiers
Muhammad Zohaib Jan and Brijesh Verma · 2019
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Improving adversarial robustness via promoting ensemble diversity
Tianyu Pang, Kun Xu, Chao Du, Ning Chen, and Jun Zhu · 2019
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Generalized negative correlation learning for deep ensembling
Sebastian Buschjäger, Lukas Pfahler, and Katharina Morik · 2020
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Ensemble deep learning in bioinformatics
Yue Cao, Thomas Andrew Geddes, Jean Yee Hwa Yang, and Pengyi Yang · 2020
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When ensembling smaller models is more efficient than single large models
Dan Kondratyuk, Mingxing Tan, Matthew Brown, and Boqing Gong · 2020
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Dibs: Diversity inducing information bottleneck in model ensembles
Samarth Sinha, Homanga Bharadhwaj, Anirudh Goyal, Hugo Larochelle, Animesh Garg, and Florian Shkurti · 2020
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Rethinking bias-variance trade-off for generalization of neural networks
Zitong Yang, Yaodong Yu, Chong You, Jacob Steinhardt, and Yi Ma · 2020
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Optimising diversity in classifier ensembles of classification trees
Carina Ivaşcu, Richard Everson, and Jonathan Fieldsend · 2021
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To ensemble or not ensemble: When does end-to-end training fail?
Andrew Webb, Charles Reynolds, Wenlin Chen, Henry Reeve, Dan Iliescu, Mikel Lujan, and Gavin Brown · 2021
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Boosting ensemble accuracy by revisiting ensemble diversity metrics
Yanzhao Wu, Ling Liu, Zhongwei Xie, Ka-Ho Chow, and Wenqi Wei · 2021
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Ensembles of classifiers: a bias-variance perspective
Neha Gupta, Jamie Smith, Ben Adlam, and Zelda E Mariet · 2022
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Diversity and generalization in neural network ensembles
Luis A Ortega, Rafael Cabañas, and Andrés R Masegosa · 2022
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The impact of averaging logits over probabilities on ensembles of neural networks
Cedrique Tassi, Jakob Gawlikowski, Auliya Fitri, and Rudolph Triebel · 2022
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Bias-variance decompositions for margin losses
Danny Wood, Tingting Mu, and Gavin Brown · 2022
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