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Outlier Ensembles - An Introduction (Paperback, Softcover reprint of the original 1st ed. 2017)
Loot Price: R2,313
Discovery Miles 23 130
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Outlier Ensembles - An Introduction (Paperback, Softcover reprint of the original 1st ed. 2017)
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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.
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