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: Outlier Ensembles
    sub title: An Introduction
  name:
    name part: Aggarwal, Charu C.
    role:
      role term: creator
    role:
      role term: author.
  name:
    name part: Sathe, Saket.
    role:
      role term: author.
  name:
    name part: SpringerLink (Online service)
  type of resource: text
  origin info:
    place:
      place term: gw
    date issued: 2017
    issuance: monographic
  language:
    language term: eng
  physical description:
    form: electronic
    form: electronic resource
    reformatting quality: access
    form: remote
    extent: XVI, 276 p. 55 illus., 9 illus. in color. online resource.
  abstract: This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. In addition, it covers the techniques with which such methods can be made more effective. A formal classification of these methods is provided, and the circumstances in which they work well are examined. The authors cover how outlier ensembles relate (both theoretically and practically) to the ensemble techniques used commonly for other data mining problems like classification. The similarities and (subtle) differences in the ensemble techniques for the classification and outlier detection problems are explored. These subtle differences do impact the design of ensemble algorithms for the latter problem. This book can be used for courses in data mining and related curricula. Many illustrative examples and exercises are provided in order to facilitate classroom teaching. A familiarity is assumed to the outlier detection problem and also to generic problem of ensemble analysis in classification. This is because many of the ensemble methods discussed in this book are adaptations from their counterparts in the classification domain. Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. Also included is an analysis of the performance of various types of base detectors and their relative effectiveness. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.
  table of contents: An Introduction to Outlier Ensembles -- Theory of Outlier Ensembles -- Variance Reduction in Outlier Ensembles -- Bias Reduction in Outlier Ensembles: The Guessing Game -- Model Combination Methods for Outlier Ensembles -- Which Outlier Detection Algorithm Should I Use?
  note: by Charu C. Aggarwal, Saket Sathe.
  subject:
    topic: Computer science
  subject:
    topic: Computers
  subject:
    topic: Artificial intelligence
  subject:
    topic: Statistics
  subject:
    topic: Computer Science
  subject:
    topic: Information Systems and Communication Service
  subject:
    topic: Artificial Intelligence (incl. Robotics)
  subject:
    topic: Statistics and Computing/Statistics Programs
  classification: QA75.5-76.95
  classification: 005.7
  related item:
    title info:
      title: Springer eBooks
  identifier: 9783319547657
  identifier: http://dx.doi.org/10.1007/978-3-319-54765-7
  record info:
    record creation date: 170406
    record change date: 20170406062611.0
    record identifier: 978-3-319-54765-7