{
  "id": "1809.05710",
  "version": "v1",
  "published": "2018-09-15T12:49:41.000Z",
  "updated": "2018-09-15T12:49:41.000Z",
  "title": "Alternate Estimation of a Classifier and the Class-Prior from Positive and Unlabeled Data",
  "authors": [
    "Masahiro Kato",
    "Liyuan Xu",
    "Gang Niu",
    "Masashi Sugiyama"
  ],
  "categories": [
    "cs.LG",
    "stat.ML"
  ],
  "abstract": "We consider a problem of learning a binary classifier only from positive data and unlabeled data (PU learning) and estimating the class-prior in unlabeled data under the case-control scenario. Most of the recent methods of PU learning require an estimate of the class-prior probability in unlabeled data, and it is estimated in advance with another method. However, such a two-step approach which first estimates the class prior and then trains a classifier may not be the optimal approach since the estimation error of the class-prior is not taken into account when a classifier is trained. In this paper, we propose a novel unified approach to estimating the class-prior and training a classifier alternately. Our proposed method is simple to implement and computationally efficient. Through experiments, we demonstrate the practical usefulness of the proposed method.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2018-09-15T12:49:41.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "unlabeled data",
      "alternate estimation",
      "two-step approach",
      "novel unified approach",
      "pu learning"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}