An Analysis of Ensemble Pruning Techniques Based on Ordered Aggregation

By Gonzalo Martínez-Muñoz, Daniel Hernández-Lobato, Alberto Suárez. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009

336 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Information Systems

View Paper: https://www.semanticscholar.org/paper/55a50e80a1f433f393576de3b09c56777fa1b69a

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Citations in the corpus

Training Efficient Tree-Based Models for Document Ranking (2013, methodology): “For an overview of ensemble pruning techniques see [19] and the references therein.”
A survey of multiple classifier systems as hybrid systems (2014, methodology): “Diversity measures usually employ the most valuable sub-ensemble in ensemble pruning processes [71].”
ForEx++: A New Framework for Knowledge Discovery from Decision Forests (2017, background): “…selection algorithms that prunes a number of decision trees from a decision forest while retaining or increasing ensemble accuracy (Adnan and Islam (2016c), Lu et al. (2010), Margineantu and Dietterich (1997), Martínez-Muñoz et al. (2009), MartínezMuñoz and Suárez (2004), Ruta and Gabrys (2005)).”
Offline Data-Driven Evolutionary Optimization Using Selective Surrogate Ensembles (2019, neutral): “on complementarity, orientation, or margin distance [69].”
Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research (2015, methodology): “Many ensemble selection strategies (e.g., Banfield et al., 2005; Martínez-Muñoz et al., 2009) would thus weight and equally when assessing the prospensity of adding to ′.”
Ensemble approaches for regression: A survey (2012, methodology): “Even though there are surveys on ensemble pruning [Tsoumakas et al. 2008; Martinez-Munoz et al. 2009], they only address classi.cation.”
A survey on ensemble learning (2019, background): “[33] studied several pruning strategies and found that an appropriately ordered classifier aggregation could better the model in terms of accuracy and robustness.”
A survey of uncertainty in deep neural networks (2021, background): “For that, several approaches based on different diversity measures are developed to remove single members without strongly affecting the performance [88], [87], [243].”
A Survey on ensemble learning under the era of deep learning (2021, methodology): “The ranking-based method first uses a certain measurement (such as accuracy) to rank the base learners, and then uses a suitable stopping criterion to select a number of base learners for ensemble (Martinez-Muñoz et al. 2009).”
On Oblique Random Forests (2011, background): “Recent results on ensemble pruning showed an advantage of choosing base learners which are optimally adapted to the data [22].”