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001 978-3-319-54765-7
003 DE-He213
005 20210118123551.0
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008 170406s2017 gw | s |||| 0|eng d
020 _a9783319547657
_9978-3-319-54765-7
024 7 _a10.1007/978-3-319-54765-7
_2doi
050 4 _aQA75.5-76.95
072 7 _aUT
_2bicssc
072 7 _aCOM069000
_2bisacsh
072 7 _aUT
_2thema
082 0 4 _a005.7
_223
100 1 _aAggarwal, Charu C.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aOutlier Ensembles
_h[electronic resource] :
_bAn Introduction /
_cby Charu C. Aggarwal, Saket Sathe.
250 _a1st ed. 2017.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2017.
300 _aXVI, 276 p. 55 illus., 9 illus. in color.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
505 0 _aAn 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?
520 _aThis 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.
650 0 _aComputers.
650 0 _aArtificial intelligence.
650 0 _aStatistics .
650 1 4 _aInformation Systems and Communication Service.
_0http://scigraph.springernature.com/things/product-market-codes/I18008
650 2 4 _aArtificial Intelligence.
_0http://scigraph.springernature.com/things/product-market-codes/I21000
650 2 4 _aStatistics and Computing/Statistics Programs.
_0http://scigraph.springernature.com/things/product-market-codes/S12008
700 1 _aSathe, Saket.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer eBooks
776 0 8 _iPrinted edition:
_z9783319547640
776 0 8 _iPrinted edition:
_z9783319547664
776 0 8 _iPrinted edition:
_z9783319854748
856 4 0 _uhttps://doi.org/10.1007/978-3-319-54765-7
912 _aZDB-2-SCS
999 _c446158
_d446158
942 _cEB
506 _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки.
506 _fOnline access from local network of NaUOA.
506 _fOnline access with authorization at https://link.springer.com/
506 _fОнлайн-доступ з локальної мережі НаУОА.
506 _fОнлайн доступ з авторизацією на https://link.springer.com/