Bridging Cross-Tasks Gap for Cognitive Assessment via Fine-Grained Domain Adaptation
Authors: Yingwei Zhang, Yiqiang Chen, Hanchao Yu, Zeping Lv, Qing Li, Xiaodong Yang
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To evaluate the performance of FAT, we conduct experiments in real clinical environments. Experimental results demonstrate that FAT is significantly more accurate and efficient compared with other state-of-the-art methods. |
| Researcher Affiliation | Academia | 1Beijing Key Laboratory of Mobile Computing and Pervasive Device, Institute of Computing Technology, Chinese Academy of Sciences 2University of Chinese Academy of Sciences 3Peng Cheng Laboratory 4Beijing Key Laboratory of Rehabilitation Technical Aids for Old-Age Disability, National Research Center for Rehabilitation Technical Aids 5School of Biological Science and Medical Engineering, Beihang University |
| Pseudocode | Yes | Algorithm 1 Modify The Subtree (MTS), Algorithm 2 Split Leaf Nodes (SLN), Algorithm 3 Update Feature Threshold (UFT), Algorithm 4 Fine-Grained Adaptation Random Forest (FAT) |
| Open Source Code | No | The paper does not provide an explicit statement about open-sourcing the code for the described methodology, nor does it provide a link to a code repository. |
| Open Datasets | No | The paper describes collecting two cognitive assessment datasets in real clinical environments, but does not provide specific access information (e.g., link, DOI, or citation for public access) for these datasets. |
| Dataset Splits | No | The paper mentions that '30% of data in the target domain are used to fine-tune the existing source domain model, and the other 70% are used as the testing data,' but does not explicitly describe a separate 'validation' split with percentages or counts for model selection or hyperparameter tuning. |
| Hardware Specification | Yes | We conduct experiments on Lenovo Think Station desktop computer (Intel Core i7-6700/16GB DDR3) with Matlab R2018b platform. |
| Software Dependencies | Yes | We conduct experiments on Lenovo Think Station desktop computer (Intel Core i7-6700/16GB DDR3) with Matlab R2018b platform. |
| Experiment Setup | Yes | We set the number of trees in random forest M = 30. The number of candidate features for each node is set as K, K is the total number of features. The minimum instance size for splitting a node is set as 2. The maximum depth for individual tress is set as max D = 10. In addition, three transfer methods for feature knowledge (i.e., STL, TCA and GFK) require dimension reduction. Therefore, we set the dimension of feature as 30. δ1, δ2, δ3 in FAT are set to 0.6, 0.7, and 0.8 respectively. |