Prediction of Mild Cognitive Impairment Conversion Using Auxiliary Information
Authors: Xiaofeng Zhu
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on the public data of Alzheimer s Disease Neuroimaging Initiative (ADNI) verified the effectiveness of our proposed method, compared to three state-of-the-art feature selection methods, in terms of four classification evaluation metrics. |
| Researcher Affiliation | Academia | Xiaofeng Zhu University of Electronic Science and Technology of China, Chengdu 611731, China. |
| Pseudocode | Yes | We list the pseudo of our method in Algorithm 1 and explain the detail as follows. ... Algorithm 1: The pseudo of solving Eq. (7). |
| Open Source Code | No | The paper does not provide any statement or link indicating that its source code is openly available. |
| Open Datasets | Yes | In this work, we used the ADNI 1 ( www.adni-info. org ) publicly available on the web for research purposes to generate the binary classification task on two data sets |
| Dataset Splits | Yes | We repeated the 10-fold cross-validation scheme 100 times on all methods, each of which conducted 5-fold nested cross-validations for model selection. |
| Hardware Specification | No | The paper does not provide any specific hardware details used for running the experiments. |
| Software Dependencies | No | The paper mentions using Support Vector Machine (SVM) and cites LIBSVM, but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The ranges of parameters of every comparison method were set by strictly following the corresponding literature so that they outputted the best results in our experiments. We used the method of grid search with the search range of {10 5, ..., 105} to conduct model selection in our two proposed methods. ... We used the Support Vector Machine (SVM) [Chang and Lin, 2011] to conduct the classification tasks, where we set the parameter C as the range of C {2 5, 2 4, . . . , 25} in the SVM for all methods. |