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生态生物学研究中不平衡数据的统计方法-Statistical Methods for Imbalanced Data in Ecological and Biological Studies

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上传于 2020-03-02 0次下载 4309次围观
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标题(title):Statistical Methods for Imbalanced Data in Ecological and Biological Studies
生态生物学研究中不平衡数据的统计方法
作者(author):Osamu Komori, Shinto Eguchi
出版社(publisher):Springer Japan
大小(size):1 MB (1538464 bytes)
格式(extension):pdf
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This 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.


Table of contents :
Front Matter ....Pages i-viii
Introduction to Imbalanced Data (Osamu Komori, Shinto Eguchi)....Pages 1-10
Weighted Logistic Regression (Osamu Komori, Shinto Eguchi)....Pages 11-25
\(\beta \)-Maxent (Osamu Komori, Shinto Eguchi)....Pages 27-33
Generalized T-Statistic (Osamu Komori, Shinto Eguchi)....Pages 35-43
Machine Learning Methods for Imbalanced Data (Osamu Komori, Shinto Eguchi)....Pages 45-55
Back Matter ....Pages 57-59
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