Outlier Ensembles

Authors: Charu C. Aggarwal (IBM T. J. Watson Research Center), Saket Sathe (IBM T. J. Watson Research Center)

This book discusses a variety of methods for outlier ensembles and organizes them by the specific principles with which accuracy improvements are achieved. 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.

Some techniques explained in this book, such as wagging, randomized feature weighting, and geometric subsampling, provide new insights that are not available elsewhere. The book is valuable for researchers and practitioners for leveraging ensemble methods into optimal algorithmic design.