Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Active Learning of Multi-Class Classification Models from Ordered Class Sets
Authors: Yanbing Xue, Milos Hauskrecht5589-5596
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate the beneο¬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 EMAIL, EMAIL |
| 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. |