Polytree-Augmented Classifier Chains for Multi-Label Classification
Authors: Lu Sun, Mineichi Kudo
IJCAI 2015 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | The experiments performed on both artificial and benchmark multi-label data sets demonstrated that the proposed method is competitive with the stateof-the-art multi-label classification methods. |
| Researcher Affiliation | Academia | Lu Sun and Mineichi Kudo Graduate School of Information Science and Technology Hokkaido University, Sapporo, Japan {sunlu, mine}@main.ist.hokudai.ac.jp |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper mentions that methods were implemented based on Weka, Mulan, and Meka, but it does not provide an explicit statement or link to the authors' own open-source code for the proposed method. |
| Open Datasets | No | The paper lists various benchmark datasets (e.g., Scene, Emotions, Yeast, etc.) in Table 2, but it does not provide specific links, DOIs, repository names, or formal citations with authors and years for these datasets, which are required for concrete access information. |
| Dataset Splits | Yes | For calculation of the accuracy, 10-fold and 3-fold cross validation were used for the regular and large data sets, respectively. |
| Hardware Specification | Yes | In a Intel Quad-Core CPU at 3.4 GHz with 8 GB RAM. |
| Software Dependencies | No | The paper mentions Weka4, Mulan5 and Meka6 as implementation bases, and 'logistic regression with L2 regularization as the baseline classifier', but it does not provide specific version numbers for any software, libraries, or dependencies. |
| Experiment Setup | Yes | In the experiments we chose logistic regression with L2 regularization as the baseline classifier, set the number of neighbors to k = 10 in MLk NN, and used 100 iterations as the burn-in time for CDN. |