Version Space Reduction Based on Ensembles of Dissimilar Balanced Perceptrons
Authors: Karen Braga Enes, Saulo Moraes Villela, Raul Fonseca Neto
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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 deļ¬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. |