Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
An urn model for majority voting in classification ensembles
Authors: Victor Soto, Alberto SuƔrez, Gonzalo Martinez-MuƱoz
NeurIPS 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In this section we present the results of an extensive empirical evaluation of the dynamical ensemble pruning method described in the previous section. The experiments are performed in a series of benchmark classiļ¬cation problems from the UCI Repository [1] and synthetic data [4] using Random Forests [5]. |
| Researcher Affiliation | Academia | Victor Soto Computer Science Department Columbia University New York, NY, USA EMAIL Alberto SuĆ”rez and Gonzalo MartĆnez-MuƱoz Computer Science Department Universidad AutĆ³noma de Madrid Madrid, Spain EMAIL |
| Pseudocode | No | No structured pseudocode or algorithm blocks were found in the paper. |
| Open Source Code | Yes | The code is available at: https://github.com/vsoto/majority-ibp-prior. |
| Open Datasets | Yes | The experiments are performed in a series of benchmark classiļ¬cation problems from the UCI Repository [1] and synthetic data [4] using Random Forests [5]. |
| Dataset Splits | Yes | for each problem, 100 partitions are created by 10 10-fold cross-validation for real datasets and by random sampling in the synthetic datasets. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU/GPU models, memory) used for running experiments. |
| Software Dependencies | No | The paper mentions using Random Forests but does not specify any software dependencies with version numbers (e.g., Python version, specific library versions). |
| Experiment Setup | Yes | (i) a Random Forest ensemble of size T = 101 is built; (iii) The SIBA algorithm [14] is applied to dynamically select the number of classiļ¬ers that are needed for each instance in the test set to achieve a level of conļ¬dence in the prediction above Ī± = 0.99. |