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 [1].
Subset Selection by Pareto Optimization with Recombination
Authors: Chao Qian, Chao Bian, Chao Feng2408-2415
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on unsupervised feature selection and sparse regression show the superiority of PORSS over POSS. |
| Researcher Affiliation | Academia | 1National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027, China EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 POSS Algorithm; Algorithm 2 PORSS Algorithm |
| Open Source Code | No | The paper provides links to datasets used in the experiments but does not provide specific links or statements regarding the open-sourcing of its own methodology's code. |
| Open Datasets | Yes | https://archive.ics.uci.edu/ml/datasets.html, https://www.csie. ntu.edu.cn/ cjlin/libsvmtools/datasets/ and http://www.cl.cam.ac. uk/research/dtg/attarchive/facedatabase.html. The paper lists datasets such as sonar, phishing, Hill-Valley, mediamill, musk, CT-slices, ISOLET, mnist, SVHN, ORL, svmguide3, triazines, clean1, usps, scene, protein, colon-cancer, cifar10, leukemia, small NORB. |
| Dataset Splits | No | The paper does not provide specific details on dataset splits (e.g., percentages, sample counts for training, validation, or testing sets), nor does it mention cross-validation. It only states that runs were repeated ten times. |
| Hardware Specification | No | The paper does not provide any specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms and models like NSGA-II and Lasso but does not provide specific software dependencies (e.g., libraries, frameworks) with version numbers needed to replicate the experiments. |
| Experiment Setup | Yes | As suggested in (Qian, Yu, and Zhou 2015), the number T of iterations of POSS is set to 2ek2n. Note that POSS in Algorithm 1 requires one objective evaluation for the newly generated solution x in each iteration, whereas PORSS in Algorithm 2 needs to evaluate two new solutions x , y . For the fairness of comparison, the number T of iterations of PORSS is set to ek2n; thus, the same number of objective evaluations is used. The budget k is set to 8. |