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 [1].
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. |