Learning Label-Specific Multiple Local Metrics for Multi-Label Classification
Authors: Jun-Xiang Mao, Jun-Yi Hang, Min-Ling Zhang
IJCAI 2024 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 4 Experiments Comprehensive experiments on benchmark multi-label datasets validate the superiority of LSMM in learning effective similarity metrics for multi-label classification. |
| Researcher Affiliation | Academia | 1School of Computer Science and Engineering, Southeast University, Nanjing 210096, China 2Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China {maojx, hangjy, zhangml}@seu.edu.cn |
| Pseudocode | Yes | The complete procedure of LSMM can be found in Appendix A. |
| Open Source Code | No | No explicit statement or link providing access to the source code for the methodology described in this paper was found. |
| Open Datasets | Yes | Nine benchmark multi-label datasets with diversified properties are employed for comprehensive performance evaluation. Table 1 summarizes the characteristics of each experimental dataset D... 1 http://mulan.sourceforge.net/datasets.html 2 http://palm.seu.edu.cn/zhangml/Resources.htm#data 3 https://waikato.github.io/meka/datasets/ |
| Dataset Splits | Yes | We take out 10% examples in each dataset as a hold out validation set for hyperparameter searching and perform ten-fold crossvalidation on the remaining 90% examples to evaluate the above approaches on the nine benchmark multi-label datasets. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, processor types, memory amounts, or detailed computer specifications) used for running experiments were provided. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.8, PyTorch 1.9, CUDA 11.1) were provided. |
| Experiment Setup | Yes | In this paper, kt and ki are set to 20. γ and α are fixed to 2 and 0.4 respectively, and the smooth hinge loss is used to instantiate ℓ( ). In this paper, C is set to 3. For the proposed LSMM-SE and LSMM-CL approaches, regularization parameters λ1 and λ2 are searched in {10 1, 1, . . . , 103} and {10 3, 10 2, . . . , 10} respectively. The number of nearest neighbors (denoted as k) in KNN and ML-KNN is set to 10. |