LEADER 04056nam 22006015 450 001 9910254842703321 005 20200630154856.0 010 $a3-319-54765-8 024 7 $a10.1007/978-3-319-54765-7 035 $a(CKB)3850000000027371 035 $a(DE-He213)978-3-319-54765-7 035 $a(MiAaPQ)EBC6311638 035 $a(MiAaPQ)EBC5577452 035 $a(Au-PeEL)EBL5577452 035 $a(OCoLC)982655949 035 $a(PPN)200514784 035 $a(EXLCZ)993850000000027371 100 $a20170406d2017 u| 0 101 0 $aeng 135 $aurnn|008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aOutlier Ensembles $eAn Introduction /$fby Charu C. Aggarwal, Saket Sathe 205 $a1st ed. 2017. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2017. 215 $a1 online resource (XVI, 276 p. 55 illus., 9 illus. in color.) 311 $a3-319-54764-X 320 $aIncludes bibliographical references and index. 327 $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? 330 $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. 606 $aComputers 606 $aArtificial intelligence 606 $aStatistics  606 $aInformation Systems and Communication Service$3https://scigraph.springernature.com/ontologies/product-market-codes/I18008 606 $aArtificial Intelligence$3https://scigraph.springernature.com/ontologies/product-market-codes/I21000 606 $aStatistics and Computing/Statistics Programs$3https://scigraph.springernature.com/ontologies/product-market-codes/S12008 615 0$aComputers. 615 0$aArtificial intelligence. 615 0$aStatistics . 615 14$aInformation Systems and Communication Service. 615 24$aArtificial Intelligence. 615 24$aStatistics and Computing/Statistics Programs. 676 $a005.1 700 $aAggarwal$b Charu C$4aut$4http://id.loc.gov/vocabulary/relators/aut$0518673 702 $aSathe$b Saket$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910254842703321 996 $aOutlier Ensembles$92517720 997 $aUNINA