Feature-Induced Labeling Information Enrichment for Multi-Label Learning
Authors: Qian-Wen Zhang, Yun Zhong, Min-Ling Zhang
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on fifteen benchmark data sets clearly validate the effectiveness of the proposed feature-induced strategy for enhancing labeling information of multi-label examples. |
| Researcher Affiliation | Collaboration | Qian-Wen Zhang, Yun Zhong, Min-Ling Zhang , , Tencent Smart Platform & Products Department, Chengdu 610041, China , School of Computer Science and Engineering, Southeast University, Nanjing 210096, China Key Laboratory of Computer Network and Information Integration (Southeast University), Ministry of Education, China Collaborative Innovation Center of Wireless Communications Technology, China |
| Pseudocode | No | The paper presents mathematical equations and describes procedures in prose, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide an explicit statement or a link to the source code for the methodology described in the paper. |
| Open Datasets | Yes | A total of fifteen benchmark multi-label data sets are employed for performance evaluation.1 Publicly available at http://mulan.sourceforge.net/datasets.html and http://meka.sourceforge.net/#datasets |
| Dataset Splits | Yes | Ten-fold cross-validation is performed on the benchmark data sets, where the mean metric value as well as standard deviation are recorded for each comparing algorithm. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU/CPU models or memory. |
| Software Dependencies | No | The paper mentions ADMM techniques, MSVR, and RBF kernel but does not provide specific version numbers for any software dependencies or libraries. |
| Experiment Setup | Yes | For MLFE, parameters β1, β2 and β3 in Eq.(6) are chosen among {1, 2, . . . , 10}, {1, 10, 15} and {1, 10} respectively with cross-validation on the training set.2 In this paper, the parameters ρ and λ in Eq.(3) are fixed to be 1 and 1 100 A i xi , the parameters c1 and c2 in Eq.(5) are fixed to be 1 and 2. Furthermore, RBF kernel is utilized to instantiate the multi-output SVR employed by MLFE. |