{
  "id": "1611.08618",
  "version": "v1",
  "published": "2016-11-25T21:33:57.000Z",
  "updated": "2016-11-25T21:33:57.000Z",
  "title": "A Benchmark and Comparison of Active Learning for Logistic Regression",
  "authors": [
    "Yazhou Yang",
    "Marco Loog"
  ],
  "comment": "18 pages, 6 figures, 4 tables",
  "categories": [
    "stat.ML"
  ],
  "abstract": "Various active learning methods based on logistic regression have been proposed. In this paper, we investigate seven state-of-the-art strategies, present an extensive benchmark, and provide a better understanding of their underlying characteristics. Experiments are carried out both on 3 synthetic datasets and 43 real-world datasets, providing insights into the behaviour of these active learning methods with respect to classification accuracy and their computational cost.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2016-11-25T21:33:57.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "logistic regression",
      "active learning methods",
      "comparison",
      "seven state-of-the-art strategies",
      "computational cost"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 18,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}