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..
BRBA: A Blocking-Based Association Rule Hiding Method
Authors: Peng Cheng, Ivan Lee, Li Li, Kuo-Kun Tseng, Jeng-Shyang Pan
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative experiments on real datasets demonstrate that the proposed method can achieve its goals. |
| Researcher Affiliation | Academia | 1 School of Computer and Information Science, Southwest University, P.R. China 2 Shenzhen Graduate School, Harbin Institute of Technology, P.R. China 3 School of Information Technology and Mathematical Sciences, University of South Australia, Australia |
| Pseudocode | No | The paper describes the BRBA algorithm but does not present it in a structured pseudocode or algorithm block format. |
| Open Source Code | No | The paper does not contain any statement about releasing source code or provide a link to a code repository. |
| Open Datasets | No | The paper mentions using "three real datasets: Mushroom, Bms-1 and Bms-2" but does not provide specific access information (link, DOI, repository) or formal citations with author names and year to indicate public availability. |
| Dataset Splits | No | The paper does not provide specific percentages or counts for training, validation, or test dataset splits. It only states that experiments were conducted on "three real datasets". |
| Hardware Specification | No | The paper does not provide any specific details regarding the hardware used for running the experiments (e.g., CPU, GPU models, memory). |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | The Safety Margin was applied in both algorithms. Fig. 2 shows the side effects of two algorithms with different A01 values (0.1, 0.3, 0.5, 0.7, 0.9). |