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..
Adaptive Sparse Confidence-Weighted Learning for Online Feature Selection
Authors: Yanbin Liu, Yan Yan, Ling Chen, Yahong Han, Yi Yang4408-4415
AAAI 2019 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 6 Experiments In this section, we evaluate the proposed ASCW algorithm on three imbalanced measures, i.e., F-measure, AUROC, and AUPRC and compare with various online learning and feature selection methods. |
| Researcher Affiliation | Academia | 1SUSTech-UTS Joint Centre of CIS, Southern University of Science and Technology 2Centre for Artificial Intelligence, University of Technology Sydney 3College of Intelligence and Computing, Tianjin University |
| Pseudocode | Yes | Algorithm 1 Imbalanced sparse CW in online-batch manner and Algorithm 2 Multiple Cost-Sensitive Learning. |
| Open Source Code | No | The paper does not provide an explicit statement or link to open-source code for the methodology described. |
| Open Datasets | Yes | We conduct experiments on three widely-used high-dimensional benchmarks and sample with different ratios to construct nine imbalance configurations, as shown in Table 1. Datasets real-sim, rcv1, news20. |
| Dataset Splits | No | The paper mentions 'training data' and 'test performance' but does not specify explicit train/validation/test splits with percentages or sample counts. It refers to 'online-batch' processing, which is a different concept from a static dataset split. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., CPU or GPU models, memory, or specific cloud instances) used for running the experiments. |
| Software Dependencies | No | The paper mentions 'Liblinear' (for L1SVM) but does not provide specific version numbers for any software dependencies or libraries used in their implementation. |
| Experiment Setup | Yes | To explain the necessity of the online-batch update and explore proper batch size, we perform experiments on news20 with various batch sizes, as shown in Table 2. The best performance is achieved with batch size=1... We thus set batch size=256 in remaining experiments. ... we set the selected feature dimension to 50 for all algorithms except that for CSOAL we set query ratio to be 1%. |