A Geometric Theory of Feature Selection and Distance-Based Measures
Authors: Kilho Shin, Adrian Pino Angulo
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We also investigate the correlation between measurements by these measures and classification accuracy through experiments. |
| Researcher Affiliation | Academia | Kilho Shin and Adrian Pino Angulo University of Hyogo Kobe, Japan yshin@ai.u-hyogo.ac.jp |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found. |
| Open Source Code | No | The paper does not provide any concrete access to source code for the described methodology. |
| Open Datasets | Yes | All of the datasets are obtained from the UCI repository of machine learning databases [Blake and Merz, 1998]. |
| Dataset Splits | Yes | We apply three classifiers, namely, Na ıve Bayes, C4.5 and SVM, to each of the localized datasets in the manner of 10-fold cross validation and record the averaged AUC-ROC scores, which we use as classification accuracy scores. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory) used for running experiments are mentioned. |
| Software Dependencies | No | The paper mentions classifiers (Na ıve Bayes, C4.5, SVM) but does not provide specific version numbers for any software components or dependencies. |
| Experiment Setup | Yes | Sampling feature sets For each dataset of Table 1, 60 feature sets are selected at random. The sizes of the selected feature sets also vary at random. In total, we obtain 60 x 12 = 720 pairs of a feature set and a dataset. ... Measuring accuracy of classification We apply three classifiers, namely, Na ıve Bayes, C4.5 and SVM, to each of the localized datasets in the manner of 10-fold cross validation and record the averaged AUC-ROC scores, which we use as classification accuracy scores. |