BRBA: A Blocking-Based Association Rule Hiding Method
Authors: Peng Cheng, Ivan Lee, Li Li, Kuo-Kun Tseng, Jeng-Shyang Pan
AAAI 2016 | Conference PDF | Archive PDF | Plain Text | 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). |