Active Learning of Multi-Class Classification Models from Ordered Class Sets
Authors: Yanbing Xue, Milos Hauskrecht5589-5596
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the beneļ¬t of the framework on multiple datasets. We show that class-order feedback and active learning can reduce the annotation cost both individually and jointly. We experiment with our new framework on both synthetic and real-world datasets with class-order feedback. |
| Researcher Affiliation | Academia | Yanbing Xue, Milos Hauskrecht Department of Computer Science University of Pittsburgh yax14@pitt.edu, milos@cs.pitt.edu |
| Pseudocode | No | The paper includes mathematical formulations but does not provide structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide any statement or link indicating that the source code for the methodology is openly available. |
| Open Datasets | Yes | The two synthetic OCS datasets are generated from UCI Vehicle Silhouettes and Optical Digits datasets. The real-world datasets consists of two Million Song datasets (CD1 and CD2) (Bertin-Mahieux et al. 2011) and one Face Sentiment dataset (Mozafari et al. 2012). |
| Dataset Splits | No | The paper mentions splitting data into "training and test set" but does not explicitly specify a separate "validation" set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper does not provide specific software dependencies with version numbers (e.g., library names with versions). |
| Experiment Setup | No | The paper does not provide specific details about the experimental setup, such as hyperparameter values (e.g., learning rate, batch size) or other system-level training configurations. |