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Statistical Methods for Imbalanced Data in Ecological and Biological Studies by

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Item specifics

Condition
Brand New: A new, unread, unused book in perfect condition with no missing or damaged pages. See all condition definitionsopens in a new window or tab
ISBN-13
9784431555698
Book Title
Statistical Methods for Imbalanced Data in Ecological and Biologi
ISBN
9784431555698
Subject Area
Mathematics, Social Science, Medical
Publication Name
Statistical Methods for Imbalanced Data in Ecology and Biology
Publisher
Springer Japan
Item Length
9.3 in
Subject
Biostatistics, Probability & Statistics / General, General, Statistics
Publication Year
2019
Series
Springerbriefs in Statistics Ser.
Type
Textbook
Format
Trade Paperback
Language
English
Author
Shinto Eguchi, Osamu Komori
Item Weight
16 Oz
Item Width
6.1 in
Number of Pages
VIII, 59 Pages

About this product

Product Identifiers

Publisher
Springer Japan
ISBN-10
4431555692
ISBN-13
9784431555698
eBay Product ID (ePID)
210289904

Product Key Features

Number of Pages
VIII, 59 Pages
Publication Name
Statistical Methods for Imbalanced Data in Ecology and Biology
Language
English
Publication Year
2019
Subject
Biostatistics, Probability & Statistics / General, General, Statistics
Type
Textbook
Author
Shinto Eguchi, Osamu Komori
Subject Area
Mathematics, Social Science, Medical
Series
Springerbriefs in Statistics Ser.
Format
Trade Paperback

Dimensions

Item Weight
16 Oz
Item Length
9.3 in
Item Width
6.1 in

Additional Product Features

Intended Audience
Scholarly & Professional
Number of Volumes
1 vol.
Illustrated
Yes
Table Of Content
1. Imbalance Data.- 2. Weighted Logistic Regression.- 3. Beta-Maxent.- 4. Generalized-t Statistic.- 5. Machine Learning Methods for Imbalance Data.
Synopsis
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., Focuses on the problem caused by imbalanced data often observed in ecology and biology Introduces the latest statistical methods for imbalanced data Demonstrates the application of statistical methods to several real data sets
LC Classification Number
QH323.5

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