Approximate and Exact Enumeration of Rule Models
Authors: Satoshi Hara, Masakazu Ishihata
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We then confirm our theoretical results through experiments on real-world data. We also show that, by using the proposed enumeration algorithms, we can find several different models of almost equal quality. and In this section, we conduct experiments to evaluate the proposed algorithms. |
| Researcher Affiliation | Academia | 1) Osaka University, Osaka, Japan 2) JST, ERATO, Kawarabayashi Large Graph Project 3) Hokkaido University, Hokkaido, Japan |
| Pseudocode | Yes | Algorithm 1 Approximate Enumeration Algorithm and Algorithm 2 Exact Enumeration Algorithm |
| Open Source Code | No | The paper does not contain an explicit statement or link to the open-source code for the methodology described in this paper. It only references third-party repositories for tools used. |
| Open Datasets | Yes | To evaluate the algorithms, we used two classification datasets of categorical data: COMPAS (Larson et al. 2016) and Mushroom (Lichman 2013). |
| Dataset Splits | No | For COMPAS, it mentions '6,489 training samples and 721 test samples'. For Mushroom, it states 'randomly split the samples into 6,499 (80%) training samples and 1,625 (20%) test samples'. While training and test splits are mentioned, there is no explicit mention of a validation split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper states 'Algorithm 1 was implemented in Python 3.5, while Algorithm 2 was implemented in C.' It does not provide specific version numbers for any libraries or frameworks used within Python or C. |
| Experiment Setup | Yes | For CORELS, we used the configurations recommended in the github repository with regularization parameter ρ = 0.015. We set the length of the rule sets to be I = 2. and In the experiment, we set the length of the rule sets to be I = 4. |