{
  "id": "1707.03909",
  "version": "v1",
  "published": "2017-07-12T21:03:36.000Z",
  "updated": "2017-07-12T21:03:36.000Z",
  "title": "Model Selection for Anomaly Detection",
  "authors": [
    "Evgeny Burnaev",
    "Pavel Erofeev",
    "Dmitry Smolyakov"
  ],
  "comment": "6 pages, 3 figures, Eighth International Conference on Machine Vision (December 8, 2015)",
  "journal": "Proc. SPIE 9875, 2015",
  "doi": "10.1117/12.2228794",
  "categories": [
    "stat.ML",
    "cs.LG",
    "stat.AP"
  ],
  "abstract": "Anomaly detection based on one-class classification algorithms is broadly used in many applied domains like image processing (e.g. detection of whether a patient is \"cancerous\" or \"healthy\" from mammography image), network intrusion detection, etc. Performance of an anomaly detection algorithm crucially depends on a kernel, used to measure similarity in a feature space. The standard approaches (e.g. cross-validation) for kernel selection, used in two-class classification problems, can not be used directly due to the specific nature of a data (absence of a second, abnormal, class data). In this paper we generalize several kernel selection methods from binary-class case to the case of one-class classification and perform extensive comparison of these approaches using both synthetic and real-world data.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2017-07-12T21:03:36.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "model selection",
      "kernel selection methods",
      "network intrusion detection",
      "two-class classification problems",
      "one-class classification algorithms"
    ],
    "tags": [
      "conference paper",
      "journal article"
    ],
    "note": {
      "typesetting": "TeX",
      "pages": 6,
      "language": "en",
      "license": "arXiv",
      "status": "editable"
    }
  }
}