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..
Version Space Reduction Based on Ensembles of Dissimilar Balanced Perceptrons
Authors: Karen Braga Enes, Saulo Moraes Villela, Raul Fonseca Neto
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct an experimental study on microarray datasets and assess the performance of the proposed method compared to Support Vector Machine and Bayes Point Machine. |
| Researcher Affiliation | Academia | 1Department of Computer Science, Universidade Federal de Juiz de Fora, Brazil 2Department of Computer Science, Universidade Federal de Minas Gerais, Brazil |
| Pseudocode | Yes | Algorithm 1 describes the Variable Margin Perceptron. Algorithm 2 describes the Version Space Reduction Machine. |
| Open Source Code | No | The paper does not provide any statement or link indicating the release of open-source code for the described methodology. |
| Open Datasets | Yes | The datasets can be found in [Glaab et al., 2012], [Zhu et al., 2007] or [Golub et al., 1999]. |
| Dataset Splits | Yes | for all the experiments, we employ a 10x10-10-fold cross-validation strategy, expect in the SVM case, where we employ a 1x10-10fold. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments. |
| Software Dependencies | No | The paper mentions software like "Sequential Minimal Optimization" and "PMCMR R package" but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The learning rate parameter was set to = 0.05, for all the Perceptrons. The number of components as 10+1 to avoid ties due to the majority voting strategy. The dissimilarity factor was deο¬ned as the highest possible value in such way that the algorithm is able to generate all required components. Table 2 summarizes the dissimilarity factors. |