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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 $aJSS Research Series in Statistics,$x2364-0057
311   $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,$x2364-0057
606   $aStatistics 
606   $aBiostatistics
606   $aStatistics for Life Sciences, Medicine, Health Sciences$3https://scigraph.springernature.com/ontologies/product-market-codes/S17030
606   $aStatistical Theory and Methods$3https://scigraph.springernature.com/ontologies/product-market-codes/S11001
606   $aBiostatistics$3https://scigraph.springernature.com/ontologies/product-market-codes/L15020
606   $aStatistics for Social Sciences, Humanities, Law$3https://scigraph.springernature.com/ontologies/product-market-codes/S17040
615  0$aStatistics .
615  0$aBiostatistics.
615 14$aStatistics for Life Sciences, Medicine, Health Sciences.
615 24$aStatistical Theory and Methods.
615 24$aBiostatistics.
615 24$aStatistics for Social Sciences, Humanities, Law.
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