Ensemble Pruning

Benchmarks archive 2025-07-28

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Datasets archive 2025-07-28

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Most implemented papers archive 2025-07-28

11 shown of 11 papers with code (27 tagged with this task in all), ordered by repositories listed in the archive, not by stars (the archive holds no stars, so PwC's “Social” and “Latest” sorts cannot be reproduced). Papers without a page here are shown as plain text.

• The Shapley Value in Machine Learning
  11 Feb 2022 3 repositories listed Syntology ran 0 of 2 samples · 2 unverified
  Over the last few years, the Shapley value, a solution concept from cooperative game theory, has found numerous applications in machine learning.
• The Shapley Value of Classifiers in Ensemble Games
  6 Jan 2021 2 repositories listed
  We argue that the Shapley value of models in these games is an effective decision metric for choosing a high performing subset of models from the ensemble.
• LLM-TOPLA: Efficient LLM Ensemble by Maximising Diversity
  4 Oct 2024 1 repository listed Syntology ran 5 of 8 samples · 3 unverified · 8 pointer-only (licence)
  This paper presents LLM-TOPLA, a diversity-optimized LLM ensemble method with three unique properties: (i) We introduce the focal diversity metric to capture the diversity-performance correlation among component LLMs of…
• Hierarchical Pruning of Deep Ensembles with Focal Diversity
  17 Nov 2023 1 repository listed
  This paper presents a novel deep ensemble pruning approach, which can efficiently identify smaller deep ensembles and provide higher ensemble accuracy than the entire deep ensemble of a large number of member networks.
• Autoselection of the Ensemble of Convolutional Neural Networks with Second-Order Cone Programming
  12 Feb 2023 1 repository listed
  Ensemble techniques are frequently encountered in machine learning and engineering problems since the method combines different models and produces an optimal predictive solution.
• Boosting Deep Ensemble Performance with Hierarchical Pruning
  7 Dec 2021 1 repository listed
  Evaluated using two benchmark datasets, we show that the proposed focal diversity powered hierarchical pruning can find significantly smaller ensembles of deep neural network models while achieving the same or better…
• Conceptually Diverse Base Model Selection for Meta-Learners in Concept Drifting Data Streams
  29 Nov 2021 1 repository listed
  Our results show that conceptual similarity thresholding has a reduced computational overhead, and yet yields comparable predictive performance to thresholding using predictive performance and MI.
• Improving the Accuracy-Memory Trade-Off of Random Forests Via Leaf-Refinement
  19 Oct 2021 1 repository listed
  In this paper, we revisit ensemble pruning in the context of `modernly' trained Random Forests where trees are very large.
• Boosting Ensemble Accuracy by Revisiting Ensemble Diversity Metrics
  19 Jun 2021 1 repository listed
  Our new metrics significantly improve the intrinsic correlation between high ensemble diversity and high ensemble accuracy.
• Sub-Architecture Ensemble Pruning in Neural Architecture Search
  1 Oct 2019 1 repository listed
  Neural architecture search (NAS) is gaining more and more attention in recent years due to its flexibility and remarkable capability to reduce the burden of neural network design.
• Ensemble Pruning based on Objection Maximization with a General Distributed Framework
  13 Jun 2018 1 repository listed
  Ensemble pruning, selecting a subset of individual learners from an original ensemble, alleviates the deficiencies of ensemble learning on the cost of time and space.

Syntology lines on 2 of the papers shown; no Syntology record for the others (a paper without an arXiv id cannot be joined to the graph, and absence from the graph layer is not a recorded non-run). “Ran” means the sample executed on a synthesized fixture, not that the paper's result was reproduced. Read from the graph 2026-09-24.