Hierarchical Classification Based on Label Distribution Learning
Authors: Changdong Xu, Xin Geng5533-5540
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on several hierarchical classification datasets show that our method significantly outperforms other state-of-the-art hierarchical classification approaches. We conduct the experiments on several hierarchical classification datasets, which demonstrate the effectiveness of our proposed method. |
| Researcher Affiliation | Academia | Changdong Xu, Xin Geng MOE Key Laboratory of Computer Network and Information Integration, School of Computer Science and Engineering, Southeast University, Nanjing 210096, China {changdongxu, xgeng}@seu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Training; Algorithm 2 Prediction |
| Open Source Code | No | The paper does not contain any statement about releasing source code for the described methodology, nor does it provide links to a code repository. |
| Open Datasets | Yes | We conduct our experiments on several hierarchical classification datasets...CLEF (Dimitrovski et al. 2011) is an image dataset...IPC is a document dataset which is a collection of patents arranged with the International Patent Classification Hierarchy. 1http://www.wipo.int/classifications/ipc/en/support/; LSHTC-small, DMOZ-2010, and DMOZ-2012 2 (Partalas et al. 2015) are a number of document datasets released from the LSHTC (Large-Scale Hierarchical Text Classification) challenges 2010 and 2012. 2http://lshtc.iit.demokritos.gr/ |
| Dataset Splits | No | The paper mentions using 'cross-validation to select the optimal parameters on the datasets' but does not specify a distinct validation set with explicit split percentages or counts for the primary dataset partitioning (e.g., 80/10/10 split). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for running the experiments, such as GPU models, CPU specifications, or memory. |
| Software Dependencies | No | The paper mentions methods like 'L-BFGS' and 'logistic regression' but does not specify any software libraries or their version numbers (e.g., Python 3.8, PyTorch 1.9, scikit-learn 0.24). |
| Experiment Setup | No | The paper mentions general settings like 'penalty parameters... are chosen with a range from 10 3 to 103' and 'α is decided in a range from 0 to 1', but it does not provide specific hyperparameter values (e.g., learning rate, batch size, number of epochs) or detailed training configurations for the final models. |