An urn model for majority voting in classification ensembles
Authors: Victor Soto, Alberto Suárez, Gonzalo Martinez-Muñoz
NeurIPS 2016 | Conference PDF | Archive PDF | Plain Text | 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 classification 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 vsoto@cs.columbia.edu Alberto Suárez and Gonzalo Martínez-Muñoz Computer Science Department Universidad Autónoma de Madrid Madrid, Spain {gonzalo.martinez,alberto.suarez}@uam.es |
| 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 classification 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 classifiers that are needed for each instance in the test set to achieve a level of confidence in the prediction above α = 0.99. |