Multilabel Classification with Label Correlations and Missing Labels
Authors: Wei Bi, James Kwok
AAAI 2014 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on a number of real-world data sets with both complete and missing labels demonstrate that the proposed algorithm can consistently outperform stateof-the-art multilabel classification algorithms. In this section, experiments are performed on five image annotation data sets3 (Table 1) used in (Guillaumin et al. 2009). |
| Researcher Affiliation | Academia | Wei Bi James T. Kwok Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong {weibi, jamesk}@cse.ust.hk |
| Pseudocode | No | The paper describes mathematical formulations and optimization subproblems, but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | Code is from http://www.cs.berkeley.edu/ bharath2/codes/ M3L/download.html, Code is from http://www.cse.msu.edu/ bucakser/software. html, Code is from http://www.cse.wustl.edu/ mchen/ (These refer to third-party code, not the proposed method's code). No explicit statement or link is provided for the authors' own open-source code for the proposed method. |
| Open Datasets | Yes | In this section, experiments are performed on five image annotation data sets3 (Table 1) used in (Guillaumin et al. 2009). 3http://lear.inrialpes.fr/people/guillaumin/data.php |
| Dataset Splits | Yes | Parameter tuning for all the methods is based on a validation set obtained by randomly sampling 30% of the training data. Results based on 5-fold cross-validation are shown in Table 2. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running its experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers. |
| Experiment Setup | Yes | Parameter tuning for all the methods is based on a validation set obtained by randomly sampling 30% of the training data. As in (Guillaumin et al. 2009), we avoid this problem by predicting as positive the five labels with the largest prediction scores. |