Dual Set Multi-Label Learning
Authors: Chong Liu, Peng Zhao, Sheng-Jun Huang, Yuan Jiang, Zhi-Hua Zhou
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To empirically evaluate the performance of our approach, we conduct experiments on two manually collected real-world datasets along with an adapted dataset. Experimental results validate the effectiveness of our approach for dual set multi-label learning. |
| Researcher Affiliation | Academia | Chong Liu,1,2 Peng Zhao,1,2 Sheng-Jun Huang,3 Yuan Jiang,1,2 Zhi-Hua Zhou1,2 1 National Key Laboratory for Novel Software Technology, Nanjing University, Nanjing 210023, China 2 Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing 210023, China 3 College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China {liuc, zhaop, jiangy, zhouzh}@lamda.nju.edu.cn, huangsj@nuaa.edu.cn |
| Pseudocode | Yes | Algorithm 1 The DSML algorithm |
| Open Source Code | No | The paper does not provide an explicit statement about the availability of source code or a link to a code repository for the described methodology. |
| Open Datasets | No | We manually collect two real-world datasets and adapt one publicly available dataset for dual set multi-label learning. Details of them can be found in a longer version. The paper names the datasets in Table 1 but does not provide any access information (link, DOI, or formal citation for the original source of the adapted dataset). |
| Dataset Splits | Yes | In this part, all algorithms are evaluated on the same five-fold partition of the same datasets. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments, such as GPU/CPU models or memory specifications. |
| Software Dependencies | No | The paper mentions several algorithms and base classifiers (e.g., RBF neural networks, ML-KNN, ML-RBF, BP-MLL, Rank SVM, Ada Boost) but does not provide specific version numbers for any software dependencies or libraries used for implementation. |
| Experiment Setup | Yes | For DSML, the number of boosting rounds T is set to 10, and B is set to 1.05. Moreover, since dual set multi-label learning is a specific case of general multi-label learning, traditional multi-label algorithms can be used for this case. Four of these algorithms are compared, which are ML-KNN (Zhang and Zhou 2007), ML-RBF (Zhang 2009), BP-MLL (Zhang and Zhou 2006), and Rank SVM (Elisseeff and Weston 2002). For these methods, hyper-parameters are set according to the suggestions given by their papers. |