Ensemble-based Ultrahigh-dimensional Variable Screening
Authors: Wei Tu, Dong Yang, Linglong Kong, Menglu Che, Qian Shi, Guodong Li, Guangjian Tian
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive simulation studies confirm our intuition that the proposed ensemble-based method is more robust against model specification than using single variable screening method. The proposed ensemble-based method is used to predict attention deficit hyperactivity disorder (ADHD) status using brain function connectivity (FC).We conduct extensive simulation studies and real data analysis to illustrate the numerical performance of proposed framework. |
| Researcher Affiliation | Collaboration | 1Department of Mathematical and Statistical Sciences, University of Alberta 2Department of Statistics and Actuarial Science, University of Waterloo 3Department of Statistics and Actuarial Science, University of Hong Kong 4Huawei Noah s Ark Lab, Hong Kong, China |
| Pseudocode | No | The paper describes the steps of its method in numbered lists and descriptive text, but it does not present a formal pseudocode block or algorithm listing. |
| Open Source Code | No | The paper does not contain any explicit statements or links indicating that open-source code for the described methodology is provided. |
| Open Datasets | Yes | We use the ADHD-200 Consortium data which is a publicly available resting-state f MRI (rs-f MRI) data [Milham et al., 2012] in this study. |
| Dataset Splits | Yes | All tuning parameter are selected by 10 folds cross validation. |
| Hardware Specification | No | The paper discusses general computing power but does not provide any specific hardware details (e.g., GPU/CPU models, memory specifications) used for running the experiments. |
| Software Dependencies | No | The paper mentions algorithms like LASSO, SCAD, and SVM but does not provide specific software names with version numbers for reproducibility. |
| Experiment Setup | No | The paper states that 'tuning parameters of LASSO and SCAD are selected' and 'All tuning parameter are selected by 10 folds cross validation', and mentions 'linear kernel' for SVM, but does not provide specific hyperparameter values or detailed training configurations. |