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Statistical Methods for Imbalanced Data in Ecological and Biological Studies

Part of the SpringerBriefs in Statistics Book Series

DOI: 10.1007/978-4-431-55570-4

ISBN: 978-4-431-55569-8

Publisher: Springer Japan - Springer Journals

Jul 3, 2019

Available

Introduction to Imbalanced Data

Komori, Osamu; Eguchi, Shinto

Pages: 1 –10

[An imbalance of sample sizes among class labels makes it difficult to obtain high classification accuracy in many scientific fields, including medical diagnosis, bioinformatics, biology, and fisheries management. This difficulty is referred to as “class imbalance problem” and is considered to be...
Available

Weighted Logistic Regression

Komori, Osamu; Eguchi, Shinto

Pages: 11 –25

[We consider an asymmetric logistic regression model as an example of a weighted logistic regression model, where the weights in the estimating equation vary according to the explanatory variables, thereby alleviating the imbalance of effective sample sizes between class labels...
Available

\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$\beta $$\end{document}-Maxent

Komori, Osamu; Eguchi, Shinto

Pages: 27 –33

[Maxent is very popular for estimating species distributions using environmental variables such as temperature, precipitation, elevation, and soil category, all of which are closely related to the habitat of the species of interest. It is designed for estimating a probability distribution that...
Available

Generalized T-Statistic

Komori, Osamu; Eguchi, Shinto

Pages: 35 –43

[We discuss a statistical method for the classification problem with two groups \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek}...
Available

Machine Learning Methods for Imbalanced Data

Komori, Osamu; Eguchi, Shinto

Pages: 45 –55

[We discuss high-dimensional data analysis in the framework of pattern recognition and machine learning, including single-component analysis and clustering analysis. Several boosting methods for tackling imbalances in sample sizes are investigated.]

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