Association Rule Hiding Based on Evolutionary Multi-Objective Optimization by Removing Items
Authors: Peng Cheng, Jeng-Shyang Pan
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Comparative experiments with exact methods on real datasets demonstrated that the proposed method can hide sensitive rules with fewer side effects. We tested the proposed algorithm on three representative real databases. ... Table 1 shows the experiment result on the above three datasets with various MCTs. We compared the two EMO-based hiding approaches with four heuristic methods... |
| Researcher Affiliation | Academia | Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong, 518055, China |
| Pseudocode | No | The paper describes the proposed method verbally but does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about the availability of its source code, nor does it include a link to a code repository. |
| Open Datasets | No | The paper mentions using 'Mushroom', 'BMS-Web View-1', and 'BMS-Web View-2' datasets for experiments. However, it does not provide concrete access information such as URLs, DOIs, or formal citations (with authors and year) to these datasets. |
| Dataset Splits | No | The paper does not explicitly provide details about training, validation, or test dataset splits, such as percentages, sample counts, or methodology for partitioning the data. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as CPU/GPU models or memory specifications. |
| Software Dependencies | No | The paper mentions using the NSGA-II and SMS-EMO algorithms but does not provide specific software dependencies or version numbers for any libraries, frameworks, or programming languages used in the implementation. |
| Experiment Setup | Yes | The paper specifies 'The population size is 40 and the maximum generation is 100' for the EMO algorithm, and mentions '5 sensitive rules were selected randomly for each dataset to perform hiding task.' It also lists minimum support (MST) and minimum confidence (MCT) thresholds in Table 1, such as 'MST=5%' and 'MCT=0.6'. |