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Imbalanced Learning: Foundations, Algorithms, and Applications-[2013]-[pdf]

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    发表于 2017-10-30 16:04:52 | 显示全部楼层 |阅读模式
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    书籍信息:
    标题: Imbalanced Learning: Foundations, Algorithms, and Applications
    语言: English
    格式: pdf
    大小: 3.9M
    页数: 216
    年份: 2013
    出版社: Wiley-IEEE Press
    DOI: 10.1002/9781118646106

    简介

    The first book of its kind to review the current status and future direction of the exciting new branch of machine learning/data mining called imbalanced learning
    Imbalanced learning focuses on how an intelligent system can learn when it is provided with imbalanced data. Solving imbalanced learning problems is critical in numerous data-intensive networked systems, including surveillance, security, Internet, finance, biomedical, defense, and more. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. The first comprehensive look at this new branch of machine learning, this book offers a critical review of the problem of imbalanced learning, covering the state of the art in techniques, principles, and real-world applications. Featuring contributions from experts in both academia and industry, Imbalanced Learning: Foundations, Algorithms, and Applications provides chapter coverage on:
    • Foundations of Imbalanced Learning
    • Imbalanced Datasets: From Sampling to Classifiers
    • Ensemble Methods for Class Imbalance Learning
    • Class Imbalance Learning Methods for Support Vector Machines
    • Class Imbalance and Active Learning
    • Nonstationary Stream Data Learning with Imbalanced Class Distribution
    • Assessment Metrics for Imbalanced Learning
    Imbalanced Learning: Foundations, Algorithms, and Applications will help scientists and engineers learn how to tackle the problem of learning from imbalanced datasets, and gain insight into current developments in the field as well as future research directions.Content:
    Chapter 1 Introduction (pages 1–12): Haibo He
    Chapter 2 Foundations of Imbalanced Learning (pages 13–41): Gary M. Weiss
    Chapter 3 Imbalanced Datasets: From Sampling to Classifiers (pages 43–59): T. Ryan Hoens and Nitesh V. Chawla
    Chapter 4 Ensemble Methods for Class Imbalance Learning (pages 61–82): Xu?Ying Liu and Zhi?Hua Zhou
    Chapter 5 Class Imbalance Learning Methods for Support Vector Machines (pages 83–99): Rukshan Batuwita and Vasile Palade
    Chapter 6 Class Imbalance and Active Learning (pages 101–149): Josh Attenberg and Seyda Ertekin
    Chapter 7 Nonstationary Stream Data Learning with Imbalanced Class Distribution (pages 151–186): Sheng Chen and Haibo He
    Chapter 8 Assessment Metrics for Imbalanced Learning (pages 187–206): Nathalie Japkowicz

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    2018-9-24 11:44
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