Overview
Provides a comprehensive overview of the field including the theory of outlier ensembles and various techniques built around this theory
Offers numerous practical insights about the implementation of ensemble methods and base algorithms
Includes exercises for students and access to a solutions manual for instructors
Includes supplementary material: sn.pub/extras
Request lecturer material: sn.pub/lecturer-material
Access this book
Tax calculation will be finalised at checkout
Other ways to access
Table of contents (6 chapters)
-
Front Matter
-
Back Matter
Keywords
About this book
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.
Authors and Affiliations
About the authors
Saket Sathe has worked at IBM Research (Australia/United States) since 2013. Saket received a Ph.D. degree in Computer Science from EPFL (Lausanne) in 2013. Before that he received a Master's (M.Tech.) degree in Electrical Engineering from the Indian Institute of Technology at Bombay and also spent one year working for a startup. His primary areas of interest are data mining and data management. Saket has served on program committees of several top-ranked conferences and has been invited to review papers for prominent peer-reviewed journals. His research has led to more than 20 papers and 5 patents. His work on sensor data management received the runner-up best-paper award in IEEE CollaborateCom 2014. He is a member of the ACM, IEEE, and the SIAM.
Bibliographic Information
Book Title: Outlier Ensembles
Book Subtitle: An Introduction
Authors: Charu C. Aggarwal, Saket Sathe
DOI: https://doi.org/10.1007/978-3-319-54765-7
Publisher: Springer Cham
eBook Packages: Computer Science, Computer Science (R0)
Copyright Information: Springer International Publishing AG 2017
Hardcover ISBN: 978-3-319-54764-0Published: 18 April 2017
Softcover ISBN: 978-3-319-85474-8Published: 25 July 2018
eBook ISBN: 978-3-319-54765-7Published: 06 April 2017
Edition Number: 1
Number of Pages: XVI, 276
Number of Illustrations: 46 b/w illustrations, 9 illustrations in colour
Topics: Information Systems and Communication Service, Artificial Intelligence, Statistics and Computing/Statistics Programs