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 Shift Toward Open and Reproducible AI Research," Under Review, 2026. doi: 10.48550/arXiv.2606.16974..
TFWT: Tabular Feature Weighting with Transformer
Authors: Xinhao Zhang, Zaitian Wang, Lu Jiang, Wanfu Gao, Pengfei Wang, Kunpeng Liu
IJCAI 2024 | Venue PDF | LLM Run Details | Input Tokens: 20,060 Total number of tokens sent to the LLM as input for this paper's analysis. | Output Tokens: 3,785 Total number of tokens produced by the LLM (including reasoning/thinking tokens) for this paper's analysis.
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Our extensive experimental results across various real-world datasets and diverse downstream tasks show the effectiveness of TFWT and highlight the potential for enhancing feature weighting in tabular data analysis. |
| Researcher Affiliation | Academia | 1Portland State University 2Computer Network Information Center, Chinese Academy of Sciences 3University of Chinese Academy of Sciences, Chinese Academy of Sciences 4Dalian Maritime University 5Jilin University |
| Pseudocode | Yes | Algorithm 1: Training of TFWT |
| Open Source Code | No | The paper does not contain any statement or link indicating that the source code for the described methodology is publicly available. |
| Open Datasets | Yes | We evaluate the proposed method with four real-world datasets: Amazon Commerce Reviews Set (AM) [Liu, 2011] from UCI... Online Shoppers Purchasing Intention Dataset (OS) [Sakar and Kastro, 2018] from UCI... MAGIC Gamma Telescope Dataset (MA) [Bock, 2007] from UCI... Smoking and Drinking Dataset with body signal (SD) [Her, 2023] from Kaggle... |
| Dataset Splits | No | For each dataset, we randomly selected between 60% and 80% as training data. The paper mentions training data but does not specify the percentage or absolute number of samples used for a dedicated validation set. |
| Hardware Specification | Yes | The models were trained on NVIDIA A100. |
| Software Dependencies | No | We implemented TFWT using Py Torch and Scikit-learn. The paper lists software used but does not provide specific version numbers for PyTorch or Scikit-learn. |
| Experiment Setup | Yes | The initial learning rate was set between 10^-3 and 10^-5. For model regularization, the dropout rate was fixed at 0.2. |