Accelerating Extreme Classification via Adaptive Feature Agglomeration
Authors: Ankit Jalan, Purushottam Kar
IJCAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experimental Results We studied the effects of using DEFRAG variants with several extreme classification algorithms, as well as compared DEFRAG with other clustering algorithms. Our implementation of DEFRAG is available at the URL given below. Code Link: https://github.com/purushottamkar/defrag/ |
| Researcher Affiliation | Academia | Ankit Jalan and Purushottam Kar Department of CSE, IIT Kanpur, INDIA aankitjalan@gmail.com, purushot@cse.iitk.ac.in |
| Pseudocode | Yes | Algorithm 1 DEFRAG: Make-Tree |
| Open Source Code | Yes | Our implementation of DEFRAG is available at the URL given below. Code Link: https://github.com/purushottamkar/defrag/ |
| Open Datasets | Yes | All datasets, train-test splits, and implementations of extreme classification algorithms were sourced from the Extreme Classification Repository [Bhatia et al., 2019]. |
| Dataset Splits | Yes | All datasets, train-test splits, and implementations of extreme classification algorithms were sourced from the Extreme Classification Repository [Bhatia et al., 2019]. |
| Hardware Specification | No | The paper states, 'Except for Di SMEC on Amazon Cat (which required 12 cores to execute scalably), all times are reported on a single core.' This mentions core usage but lacks specific hardware details such as CPU models, GPU types, or memory specifications. |
| Software Dependencies | No | For SCBC, LSC and ITDC, public implementations were not available and scalable implementations were created in the Python language. The paper does not provide specific version numbers for Python or any other software libraries or dependencies used in the experiments. |
| Experiment Setup | Yes | Hyperparameters. If available, hyperparameter settings recommended by authors were used for all methods. If unavailable, a fine grid search was performed over a reasonable range to offer adequately tuned hyperparameters to the methods. DEFRAG had its only hyperparameter, the max size of a feature cluster d0 (see Algorithm 1), fixed to 8. |