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Outlier ensemblesan introduction /

Aggarwal, Charu C.

Outlier ensemblesan introduction /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Outlier ensemblesby Charu C. Aggarwal, Saket Sathe.
其他題名:	an introduction /
作者:	Aggarwal, Charu C.
其他作者:	Sathe, Saket.
出版者:	Cham :Springer International Publishing :2017.
面頁冊數:	xvi, 276 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer eBooks
標題:	Computer algorithms.
電子資源:	http://dx.doi.org/10.1007/978-3-319-54765-7
ISBN:	9783319547657$q(electronic bk.)

Outlier ensemblesan introduction /
Aggarwal, Charu C.

Outlier ensemblesan introduction /[electronic resource] :by Charu C. Aggarwal, Saket Sathe. - Cham :Springer International Publishing :2017. - xvi, 276 p. :ill. (some col.), digital ;24 cm.

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?

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.

ISBN: 9783319547657$q(electronic bk.)

Standard No.: 10.1007/978-3-319-54765-7doiSubjects--Topical Terms:

184478
Computer algorithms.

LC Class. No.: QA76.9.A43

Dewey Class. No.: 005.1

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