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..
Safe Sample Screening for Robust Support Vector Machine
Authors: Zhou Zhai, Bin Gu, Xiang Li, Heng Huang6981-6988
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on a variety of benchmark datasets verify that our safe sample screening rules can significantly reduce the computational time. |
| Researcher Affiliation | Collaboration | Zhou Zhai,1 Bin Gu,1,2* Xiang Li,3 Heng Huang4 1School of Computer & Software, Nanjing University of Information Science & Technology, P.R.China 2JD Finance America Corporation 3Computer Science Department, University of Western Ontario, Canada 4Computer Engineering, University of Pittsburgh, USA |
| Pseudocode | Yes | Algorithm 1 Safe sample screening for single CIL problem; Algorithm 2 Safe sample screening for successive CIL problem |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. It mentions LIBSVM as a source for datasets and for comparison, which is a third-party tool. |
| Open Datasets | Yes | Table 2: The benchmark datasets used in the experiments. Dateset Dimensionality Samples Source Cod RNA 8 59535 LIBSVM a9a 123 32561 LIBSVM letter 16 20000 LIBSVM ijcnn1 22 49990 LIBSVM; 1https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/. |
| Dataset Splits | No | The paper mentions selecting 20000 samples for training, but does not specify any training/validation/test dataset splits or percentages. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, processor types, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper states: "We implement our algorithm in MATLAB." However, it does not provide specific version numbers for MATLAB or any other software libraries or dependencies. |
| Experiment Setup | Yes | The parameter C is selected from the set {0.1, 1, 10, 100}. The Gaussian kernel parameter Îș is selected from the set {0.05, 0.5, 5}. The ramp loss function parameter s is fixed at 0. The optimization precision Ï” is set to be 10 8. We typically start sample screening after 50 iterations and screen the samples every 10 iterations. |