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..
Learning Sparse Confidence-Weighted Classifier on Very High Dimensional Data
Authors: Mingkui Tan, Yan Yan, Li Wang, Anton Van Den Hengel, Ivor W. Tsang, Qinfeng (Javen) Shi
AAAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experimental results demonstrate the superior performance of the proposed methods over state-of-the-art counterparts on classification and feature selection tasks. Experiments We conduct two sets of experiments to verify our methods. |
| Researcher Affiliation | Academia | ACVT, The University of Adelaide, Australia QCIS, University of Technology Sydney, Australia Department of Mathematics, Statistics, and Computer Science, University of Illinois at Chicago, USA EMAIL, EMAIL, EMAIL |
| Pseudocode | Yes | Algorithm 1: Learning confidence-weighted classifiers in online-batch. Algorithm 2: Sparse CW learning in online-batch. Algorithm 3: Online proximal primal-dual coordinate ascent for solving problem (13) |
| Open Source Code | Yes | The sources of our methods are available from http://www.tanmingkui.com/sbcw.html. |
| Open Datasets | Yes | All data sets are widely used benchmarks in machine learning, and are summarized in Table 1. URL is originally from http://sysnet.ucsd.edu/projects/url/ for identifying suspicious URLs, astro-ph is from (Hsieh et al. 2008), and others are from http://www.csie.ntu.edu.tw/ cjlin/libsvmtools/datasets/. |
| Dataset Splits | Yes | For all competing methods, we apply 5-cross-validation to choose their parameters. |
| Hardware Specification | Yes | All experiments are conducted on a PC installed a 64-bit operating system with an Intel(R) Core(TM) Xeon CPU 3.00GHz and 32GB memory. |
| Software Dependencies | No | The paper mentions software like 'Liblinear package' and 'libol.stevenhoi.org' but does not provide specific version numbers for these software dependencies, only general links or names. |
| Experiment Setup | Yes | Require: Parameters r, C > 0, and μ = 0 and Σ = I. (Algorithm 1) in our experiments on feature selection task, we stop SBCW after miter = 15 iterations, and set r = p/miter in order to select p features |