{
  "id": "1309.6818",
  "version": "v1",
  "published": "2013-09-26T12:35:03.000Z",
  "updated": "2013-09-26T12:35:03.000Z",
  "title": "Boosting in the presence of label noise",
  "authors": [
    "Jakramate Bootkrajang",
    "Ata Kaban"
  ],
  "comment": "Appears in Proceedings of the Twenty-Ninth Conference on Uncertainty in Artificial Intelligence (UAI2013)",
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost's robustness against labelling errors. One is to employ a label-noise robust classifier as a base learner, while the other is to modify the AdaBoost algorithm to be more robust. Empirical evaluation shows that a committee of robust classifiers, although converges faster than non label-noise aware AdaBoost, is still susceptible to label noise. However, pairing it with the new robust Boosting algorithm we propose here results in a more resilient algorithm under mislabelling.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2013-09-26T12:35:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "label noise",
      "non label-noise aware adaboost",
      "label-noise robust classifier",
      "adaboosts robustness",
      "resilient algorithm"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2013arXiv1309.6818B"
    }
  }
}