{
  "id": "1207.4132",
  "version": "v1",
  "published": "2012-07-11T14:51:03.000Z",
  "updated": "2012-07-11T14:51:03.000Z",
  "title": "MOB-ESP and other Improvements in Probability Estimation",
  "authors": [
    "Rodney Nielsen"
  ],
  "comment": "Appears in Proceedings of the Twentieth Conference on Uncertainty in Artificial Intelligence (UAI2004)",
  "categories": [
    "cs.LG",
    "cs.AI",
    "stat.ML"
  ],
  "abstract": "A key prerequisite to optimal reasoning under uncertainty in intelligent systems is to start with good class probability estimates. This paper improves on the current best probability estimation trees (Bagged-PETs) and also presents a new ensemble-based algorithm (MOB-ESP). Comparisons are made using several benchmark datasets and multiple metrics. These experiments show that MOB-ESP outputs significantly more accurate class probabilities than either the baseline BPETs algorithm or the enhanced version presented here (EB-PETs). These results are based on metrics closely associated with the average accuracy of the predictions. MOB-ESP also provides much better probability rankings than B-PETs. The paper further suggests how these estimation techniques can be applied in concert with a broader category of classifiers.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2012-07-11T14:51:03.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "current best probability estimation trees",
      "improvements",
      "class probability estimates",
      "accurate class probabilities",
      "baseline bpets algorithm"
    ],
    "tags": [
      "conference paper"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2012arXiv1207.4132N"
    }
  }
}