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 | Conference PDF | Archive PDF | Plain Text | 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 {mingkui.tan, anton.vandenhengel, javen.shi}@adelaide.edu.au, {yan.yan,ivor.tsang}@uts.edu.au, liwang8@uic.edu |
| 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 |