Subset Selection by Pareto Optimization with Recombination
Authors: Chao Qian, Chao Bian, Chao Feng2408-2415
AAAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 qianc@lamda.nju.edu.cn, chaobian12@gmail.com, chaofeng@mail.ustc.edu.cn |
| 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. |