Classification with Label Distribution Learning
Authors: Jing Wang, Xin Geng
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Finally, we compare LDL4C with existing LDL algorithms on 17 real-word datasets, and experimental results demonstrate the effectiveness of LDL4C in classification. 5 Experiments 5.1 Experimental Configuration Real-word datasets. The experiments are extensively conducted on seventeen datasets totally... |
| Researcher Affiliation | Academia | Jing Wang and Xin Geng MOE Key Laboratory of Computer Network and Information Integration School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {wangjing91, xgeng}@seu.edu.cn |
| Pseudocode | No | The paper describes the proposed method and its optimization process (Section 3.5) using mathematical equations and descriptions, but it does not include any clearly labeled pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any explicit statement about making the source code for its methodology publicly available, nor does it include a link to a code repository. |
| Open Datasets | Yes | The experiments are extensively conducted on seventeen datasets totally, among which fifteen are from [Geng, 2016], and M2B is from [Nguyen et al., 2012], and SCUT-FBP is from [Xie et al., 2015]. |
| Dataset Splits | Yes | Finally, all algorithms are examined on 17 datasets with 10-fold cross validation, and average performance is reported. |
| Hardware Specification | No | The paper does not provide any specific details about the hardware (e.g., GPU/CPU models, memory) used to run the experiments. |
| Software Dependencies | No | The paper does not specify any software dependencies with version numbers (e.g., programming languages, libraries, or specific frameworks with their versions) that would be needed to replicate the experiment environment. |
| Experiment Setup | Yes | For LDL4C, the balance parameter C1 and C2 are selected from {0.001, 0.01, 0.1, 1, 10, 100} and ρ is chosen from {0.001, 0.01, 0.1} by cross validation. Moreover for AABP, the number of hidden-layer neurons is set to 64, and for AA-k NN, the number of nearest neighbors k is selected from {3, 5, 7, 9, 11}. |