Fetching the paper…
Reading the bibliography…
Machine Learning (ML) has been successfully applied to a wide range of domains and applications.
A coefficient of agreement for nominal scales
Jacob Cohen · 1960
Earlier work this paper cites.
Stacked generalization
David H Wolpert · 1992
Earlier work this paper cites.
Bagging predictors
Leo Breiman · 1996
Earlier work this paper cites.
The lack of a priori distinctions between learning algorithms
David H Wolpert · 1996
Earlier work this paper cites.
Combination of multiple classifiers using local accuracy estimates
Kevin Woods, W Philip Kegelmeyer Jr, and Kevin Bowyer · 1997
Earlier work this paper cites.
The random subspace method for constructing decision forests
Tin Kam Ho · 1998
Earlier work this paper cites.
An empirical comparison of voting classification algorithms: Bagging, boosting, and variants
Eric Bauer and Ron Kohavi · 1999
Earlier work this paper cites.
Tell me who can learn you and i can tell you who you are: Landmarking various learning algorithms
Bernhard Pfahringer, Hilan Bensusan, and Christophe Giraud-Carrier · 2000
Earlier work this paper cites.
Ensembling neural networks: many could be better than all
Zhi-Hua Zhou, Jianxin Wu, and Wei Tang · 2002
Earlier work this paper cites.
Ranking learning algorithms: Using ibl and meta-learning on accuracy and time results
Pavel B Brazdil, Carlos Soares, and Joaquim Pinto Da Costa · 2003
Earlier work this paper cites.
The data mining advisor: meta-learning at the service of practitioners
Christophe Giraud-Carrier · 2005
Earlier work this paper cites.
Predicting relative performance of classifiers from samples
Rui Leite and Pavel Brazdil · 2005
Earlier work this paper cites.
Statistical comparisons of classifiers over multiple data sets
Janez Demšar · 2006
Cited alongside, same era.
Metalearning: Applications to data mining
Pavel Brazdil, Christophe Giraud Carrier, Carlos Soares, and Ricardo Vilalta · 2008
Cited alongside, same era.
From dynamic classifier selection to dynamic ensemble selection
Albert HR Ko, Robert Sabourin, and Alceu Souza Britto Jr · 2008
Cited alongside, same era.
Learning to rank for information retrieval
Tie-Yan Liu · 2009
Cited alongside, same era.
An analysis of ensemble pruning techniques based on ordered aggregation
Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, and Alberto Suárez · 2009
Cited alongside, same era.
Sequential model-based optimization for general algorithm configuration
Frank Hutter, Holger Hoos, and Kevin Leyton-Brown · 2011
Cited alongside, same era.
Auto-weka: Combined selection and hyperparameter optimization of classification algorithms
Chris Thornton, Frank Hutter, Holger H Hoos, and Kevin Leyton-Brown · 2013
Later among the works it cites.
Dynamic selection of classifiers—a comprehensive review
Alceu S Britto, Robert Sabourin, and Luiz ES Oliveira · 2014
Later among the works it cites.
Do we need hundreds of classifiers to solve real world classification problems?
Manuel Fernández-Delgado, Eva Cernadas, Senén Barro, and Dinani Amorim · 2014
Later among the works it cites.
Sequential model-based ensemble optimization
Alexandre Lacoste, Hugo Larochelle, Mario Marchand, and François Laviolette · 2014
Later among the works it cites.
Openml: networked science in machine learning
Joaquin Vanschoren, Jan N Van Rijn, Bernd Bischl, and Luis Torgo · 2014
Later among the works it cites.
Efficient and robust automated machine learning
Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Springenberg, Manuel Blum, and Frank Hutter · 2015
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A new dataset evaluation method based on category overlap
Sejong Oh · 2011
Cited alongside, same era.
Detecting novel associations in large data sets
David N Reshef, Yakir A Reshef, Hilary K Finucane, Sharon R Grossman, Gilean McVean, Peter J Turnbaugh, Eric S Lander, Michael Mitzenmacher, and Pardis C Sabeti · 2011
Cited alongside, same era.
Ensemble approaches for regression: A survey
João Mendes-Moreira, Carlos Soares, Alípio Mário Jorge, and Jorge Freire De Sousa · 2012
Cited alongside, same era.
How large should ensembles of classifiers be?
Daniel Hernández-Lobato, Gonzalo MartíNez-MuñOz, and Alberto Suárez · 2013
Cited alongside, same era.
Pairwise meta-rules for better meta-learning-based algorithm ranking
Quan Sun and Bernhard Pfahringer · 2013
Cited alongside, same era.
Chade: Metalearning with classifier chains for dynamic combination of classifiers
Fábio Pinto, Carlos Soares, and João Mendes-Moreira
Cited in the paper.
Later among the works it cites.
Pareto ensemble pruning
Chao Qian, Yang Yu, and Zhi-Hua Zhou · 2015
Later among the works it cites.
Xgboost: A scalable tree boosting system
Tianqi Chen and Carlos Guestrin · 2016
Later among the works it cites.
Bayesian hyperparameter optimization for ensemble learning
Julien-Charles Lévesque, Christian Gagné, and Robert Sabourin · 2016
Later among the works it cites.
Hyperband: A novel bandit-based approach to hyperparameter optimization
Lisha Li, Kevin Jamieson, Giulia DeSalvo, Afshin Rostamizadeh, and Ameet Talwalkar · 2016
Later among the works it cites.
Automated machine learning: a paradigm shift that accelerates data scientist productivity at airbnb
Hamel Husain · 2017
Closest in time.