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..
Robust Flexible Feature Selection via Exclusive L21 Regularization
Authors: Di Ming, Chris Ding
IJCAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on twelve benchmark datasets demonstrate the effectiveness of the proposed regularization and the optimization algorithm as compared to state-of-the-arts. |
| Researcher Affiliation | Academia | Di Ming and Chris Ding Department of Computer Science and Engineering, University of Texas at Arlington, USA EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1 Search the largest coordinate τ of S. and Algorithm 2 ALM based optimization algorithm for solving the exclusive ℓ2,1 regularization in problem (14). |
| Open Source Code | No | No explicit statement about releasing the source code for the described methodology or a link to a code repository was found. |
| Open Datasets | Yes | Experiments on twelve benchmark datasets are conducted to evaluate the performance of feature selection methods on classification. Among those benchmarks, there are 4 image datasets: MNIST3 [Lecun et al., 1998], Yale4, Yale B5 , PIE [Sim et al., 2002]; 1 spoken letter recognition dataset: ISOLET6; 5 bio-microarray datasets: Carcinomas [Yang et al., 2006], Lung [Bhattacharjee et al., 2001], Glioma [Nutt et al., 2003], TOX6, Tumor-14 [Ramaswamy et al., 2001]; and 2 text datasets: CNAE-9 [Ciarelli and Oliveira, 2009], 20Newsgroups7. Footnotes provide links: http://vision.ucsd.edu/content/yale-face-database, http://www.cad.zju.edu.cn/home/dengcai/Data/Face Data.html, http://featureselection.asu.edu/datasets.php, http://qwone.com/ jason/20Newsgroups/ |
| Dataset Splits | Yes | To evaluate the performance on classification, 5-fold crossvalidation accuracy with SVM as classifier are computed on average. |
| Hardware Specification | No | No specific hardware details such as GPU/CPU models, memory, or cloud instance types used for experiments were mentioned in the paper. |
| Software Dependencies | No | The paper mentions 'LIBSVM [Chang and Lin, 2011] is used as the practical implementation of SVM', but it does not provide a specific version number for LIBSVM or any other software dependencies. |
| Experiment Setup | Yes | LIBSVM [Chang and Lin, 2011] is used as the practical implementation of SVM, where kernel is set as linear and parameter C is set as 1 for all the experiments. For the proposed algorithm, parameters are initialized as: t = 0, νt = 1/ X F , ρ = 1.1, ϵ1 = 1e 8, ϵ2 = 1e 5, Λt = 0, random initialization weights Wt. Also, for convergence study, α=1, β =1. |