Imbalanced Learning: Foundations, Algorithms, and Applications (Hardcover)

Haibo He, Yunqian Ma

  • 出版商: IEEE
  • 出版日期: 2013-07-01
  • 售價: $4,780
  • 貴賓價: 9.5$4,541
  • 語言: 英文
  • 頁數: 216
  • 裝訂: Hardcover
  • ISBN: 1118074629
  • ISBN-13: 9781118074626
  • 相關分類: Algorithms-data-structures
  • 海外代購書籍(需單獨結帳)

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商品描述

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.

商品描述(中文翻譯)

這是一本首次回顧目前狀態並探討未來發展方向的機器學習/資料探勘新領域書籍。該領域稱為「不平衡學習」,專注於當智能系統面對不平衡的資料時如何進行學習。解決不平衡學習問題在許多資料密集型網絡系統中至關重要,包括監控、安全、互聯網、金融、生物醫學、國防等領域。由於不平衡資料集的固有複雜特性,從這樣的資料中進行學習需要新的理解、原則、演算法和工具,以有效地將大量原始資料轉化為資訊和知識表示。

這本書是對這個新興機器學習領域的首次全面研究,詳細介紹了不平衡學習的問題,包括技術、原則和實際應用的最新發展。書中邀請了學術界和工業界的專家共同撰寫,內容包括:


  • 不平衡學習的基礎

  • 從取樣到分類器的不平衡資料集

  • 用於類別不平衡學習的集成方法

  • 支援向量機的類別不平衡學習方法

  • 類別不平衡和主動學習

  • 具有不平衡類別分佈的非穩態流資料學習

  • 評估不平衡學習的指標

《不平衡學習:基礎、演算法和應用》將幫助科學家和工程師學習如何應對從不平衡資料集中進行學習的問題,並瞭解該領域的最新發展和未來研究方向。