Multi-label Classification via Feature-aware Implicit Label Space Encoding
Authors: Zijia Lin, Guiguang Ding, Mingqing Hu, Jianmin Wang
ICML 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments conducted on benchmark datasets well demonstrate its effectiveness. |
| Researcher Affiliation | Academia | 1Department of Computer Science and Technology, Tsinghua University, Beijing, P.R. China 2School of Software, Tsinghua University, Beijing, P.R. China 3Institute of Computing Technology, Chinese Academy of Sciences, Beijing, P.R. China |
| Pseudocode | Yes | Algorithm 1 Implementation of Fa IE |
| Open Source Code | No | The paper does not mention releasing source code for the described methodology or provide any links to a code repository. |
| Open Datasets | Yes | To validate the proposed Fa IE, we download three benchmark datasets in different domains with relatively large vocabularies from Mulan (Tsoumakas et al., 2011) for experiments, i.e. delicious, CAL500, and mediamill. [...] Moreover, following (Hsu et al., 2009), we also conduct experiments on a randomly selected subset of the image dataset ESPGame, and take those tags appearing at least twice in the subset to form a much larger vocabulary. |
| Dataset Splits | Yes | In our experiments, each dataset is evenly and randomly divided into 5 parts. And then we perform 5 runs for each algorithm on it, taking one part for test and the rest for training in each run without duplication. [...] Moreover, for each run of any algorithm, we also conduct 5-fold cross validation on the training set for selecting model parameters via grid search in predefined value ranges. |
| Hardware Specification | Yes | All algorithms are conducted with Matlab on a server with an Intel Xeon E5620 CPU and 24G RAM, except that BR with L-SVM is conducted using LIBLINEAR (Fan et al., 2008). |
| Software Dependencies | No | The paper mentions 'Matlab' and 'LIBLINEAR' as software used, but does not provide specific version numbers for these components. |
| Experiment Setup | Yes | Specifically, α in the proposed Fa IE is selected from {10 1, 100, . . . , 104}, τ for MLC-BMa D is chosen from {0.1, 0.2, . . . , 1.0}, and the predefined sparsity level in CS is selected from {1, 2, . . . , M} with M being the maximal number of labels in an instance, etc. |