Hypergraph Learning With Cost Interval Optimization
Authors: Xibin Zhao, Nan Wang, Heyuan Shi, Hai Wan, Jin Huang, Yue Gao
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the effectiveness of the proposed method, we have conducted experiments on two groups of dataset, i.e., the NASA Metrics Data Program (NASA) dataset and UCI Machine Learning Repository (UCI) dataset. Experimental results and comparisons with state-of-the-art methods have exhibited better performance of our proposed method. |
| Researcher Affiliation | Academia | Key Laboratory for Information System Security, Ministry of Education Tsinghua National Laboratory for Information Science and Technology School of Software, Tsinghua University, China. {n-wang16,shi}@mails.tsinghua.edu.cn {zxb,wanhai,huangjin,gaoyue}@tsinghua.edu.cn |
| Pseudocode | Yes | Algorithm 1 The workflow of our proposed CIHL method. |
| Open Source Code | No | The paper does not provide any specific repository links or explicit statements about the availability of the source code for the methodology described. |
| Open Datasets | Yes | the widely used eight data from NASA Metrics Data Program (NASA) dataset (Menzies, Greenwald, and Frank 2007), including CM1, KC3, MC2, MW1, PC1, PC3, PC4, PC5 and seven data from binary UCI Machine Learning Repository (UCI)(Lichman 2013), including haberman, heartstatlog, sonar, SPET, SPECTF, wdbc, wpbc |
| Dataset Splits | Yes | In experiments, we randomly divide the data into three parts, i.e., 1/3 data for training, 1/3 data for testing, and 1/3 data for validation. |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU/CPU models, processor types, or memory amounts used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies or library names with version numbers needed to replicate the experiment. |
| Experiment Setup | Yes | Both of the two parameters, i.e., λ, μ, are selected for each cost interval from the set of 0.01, 0.1, 1, 10, 100. α in equation (3) is set as 0.05 in our experiments. |