Return to home page
Searching: Muskingum library catalog
Some OPAL libraries remain closed or are operating at reduced service levels. Materials from those libraries may not be requestable; requested items may take longer to arrive. Note that pickup procedures may differ between libraries. Please contact your library for new procedures, specific requests, or other assistance.
  Previous Record Previous Item Next Item Next Record
  Reviews, Summaries, etc...
Author Aggarwal, Charu C.,
Title Outlier ensembles : an introduction / Charu C. Aggarwal, Saket Sathe.
Imprint Cham, Switzerland : Springer, 2017.

View online
View online
Author Aggarwal, Charu C.,
Subject Computer algorithms.
Data mining.
Alt Name Sathe, Saket,
Description 1 online resource (xvi, 276 pages) : illustrations (some color)
polychrome rdacc
Bibliography Note Includes bibliographical references and index.
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?
Summary 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.
Note Online resource; title from PDF title page (SpringerLink, viewed April 18, 2017).
ISBN 9783319547657 (electronic bk.)
3319547658 (electronic bk.)
9783319547640 (print)
ISBN/ISSN 10.1007/978-3-319-54765-7
OCLC # 982655949
Additional Format Print version: Aggarwal, Charu C. Outlier ensembles. Cham, Switzerland : Springer, 2017 331954764X 9783319547640 (OCoLC)971357286

If you experience difficulty accessing or navigating this content, please contact the OPAL Support Team