LEADER 01099nam0-22003371i-450- 001 990002051360403321 005 20071119132223.0 035 $a000205136 035 $aFED01000205136 035 $a(Aleph)000205136FED01 035 $a000205136 100 $a20030910d1983----km-y0itay50------ba 101 0 $aita 102 $aIT 105 $aY-------001yy 200 1 $aAtti del 14. congresso della Società italiana di biologia marina$eNapoli-Sorrento, 20-24 settembre 1982 210 $aSorrento$cTipolitogr. Gutemberg$dstampa 1983 215 $a367 p.$d24 cm 300 $aSuppl. al Bollettino dei Musei e degli Istituti Biologici dell'Università di Genova, n.50 (1982) 610 0 $aBiologia marina 676 $a574.92$v11 rid. 710 02$aSocietà italiana di biologia marina$0304986 801 0$aIT$bUNINA$gRICA$2UNIMARC 901 $aBK 912 $a990002051360403321 952 $a61 VII A.5/72$fDAGEN 952 $aL 427$bs.i.$fDSS 959 $aDAGEN 959 $aDSS 996 $aAtti del 14. congresso della Società italiana di biologia marina$9406650 997 $aUNINA LEADER 03660nam 22005415 450 001 9910349351303321 005 20250504233719.0 010 $a4-431-55570-6 024 7 $a10.1007/978-4-431-55570-4 035 $a(CKB)4100000008618310 035 $a(MiAaPQ)EBC5808261 035 $a(DE-He213)978-4-431-55570-4 035 $a(PPN)238486370 035 $a(EXLCZ)994100000008618310 100 $a20190702d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Methods for Imbalanced Data in Ecological and Biological Studies /$fby Osamu Komori, Shinto Eguchi 205 $a1st ed. 2019. 210 1$aTokyo :$cSpringer Japan :$cImprint: Springer,$d2019. 215 $a1 online resource (63 pages) 225 1 $aJSS Research Series in Statistics,$x2364-0065 311 08$a4-431-55569-2 327 $a1. Imbalance Data -- 2. Weighted Logistic Regression -- 3. Beta-Maxent -- 4. Generalized-t Statistic -- 5. Machine Learning Methods for Imbalance Data. 330 $aThis book presents a fresh, new approach in that it provides a comprehensive recent review of challenging problems caused by imbalanced data in prediction and classification, and also in that it introduces several of the latest statistical methods of dealing with these problems. The book discusses the property of the imbalance of data from two points of view. The first is quantitative imbalance, meaning that the sample size in one population highly outnumbers that in another population. It includes presence-only data as an extreme case, where the presence of a species is confirmed, whereas the information on its absence is uncertain, which is especially common in ecology in predicting habitat distribution. The second is qualitative imbalance, meaning that the data distribution of one population can be well specified whereas that of the other one shows a highly heterogeneous property. A typical case is the existence of outliers commonly observed in gene expression data, and another is heterogeneous characteristics often observed in a case group in case-control studies. The extension of the logistic regression model, maxent, and AdaBoost for imbalanced data is discussed, providing a new framework for improvement of prediction, classification, and performance of variable selection. Weights functions introduced in the methods play an important role in alleviating the imbalance of data. This book also furnishes a new perspective on these problem and shows some applications of the recently developed statistical methods to real data sets. 410 0$aJSS Research Series in Statistics,$x2364-0065 606 $aBiometry 606 $aStatistics 606 $aSocial sciences$xStatistical methods 606 $aBiostatistics 606 $aStatistical Theory and Methods 606 $aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy 615 0$aBiometry. 615 0$aStatistics. 615 0$aSocial sciences$xStatistical methods. 615 14$aBiostatistics. 615 24$aStatistical Theory and Methods. 615 24$aStatistics in Social Sciences, Humanities, Law, Education, Behavorial Sciences, Public Policy. 676 $a519.5 700 $aKomori$b Osamu$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781821 702 $aEguchi$b Shinto$4aut$4http://id.loc.gov/vocabulary/relators/aut 906 $aBOOK 912 $a9910349351303321 996 $aStatistical Methods for Imbalanced Data in Ecological and Biological Studies$92536041 997 $aUNINA