rdf-syntax-grammar
mods:
  schema location: http://www.loc.gov/mods/v3 http://www.loc.gov/standards/mods/v3/mods-3-2.xsd
  title info:
    title: Statistical Methods for Imbalanced Data in Ecological and Biological Studies
  name:
    name part: Komori, Osamu.
    role:
      role term: creator
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: Eguchi, Shinto.
    role:
      role term: author.
    role:
      role term: aut
    role:
      role term: http://id.loc.gov/vocabulary/relators/aut
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: ja
    date issued: 2019
    edition: 1st ed. 2019.
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: VIII, 59 p. 22 illus., 7 illus. in color. online resource.
  abstract: This book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets.
  table of contents: 1. Imbalance Data -- 2. Weighted Logistic Regression -- 3. Beta-Maxent -- 4. Generalized-t Statistic -- 5. Machine Learning Methods for Imbalance Data.
  note: by Osamu Komori, Shinto Eguchi.
  subject:
    topic: Statistics
  subject:
    topic: Biostatistics
  subject:
    topic: Statistics for Life Sciences, Medicine, Health Sciences
  subject:
    topic: Statistical Theory and Methods
  subject:
    topic: Biostatistics
  subject:
    topic: Statistics for Social Sciences, Humanities, Law
  classification: QA276-280
  classification: 519.5
  related item:
    title info:
      title: Springer eBooks
  related item:
    title info:
      title: JSS Research Series in Statistics
  identifier: 9784431555704
  identifier: https://doi.org/10.1007/978-4-431-55570-4
  record info:
    record creation date: 190702
    record change date: 20191220125736.0
    record identifier: 978-4-431-55570-4