Cost-Sensitive Machine Learning (Hardcover)

Balaji Krishnapuram, Shipeng Yu, R. Bharat Rao

  • 出版商: CRC
  • 出版日期: 2011-12-19
  • 售價: $3,200
  • 貴賓價: 9.5$3,040
  • 語言: 英文
  • 頁數: 331
  • 裝訂: Hardcover
  • ISBN: 1439839255
  • ISBN-13: 9781439839256
  • 相關分類: Machine Learning
  • 其他版本: Cost-Sensitive Machine Learning
  • 立即出貨 (庫存=1)

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商品描述

In machine learning applications, practitioners must take into account the cost associated with the algorithm. These costs include:

 

 

  • Cost of acquiring training data
  • Cost of data annotation/labeling and cleaning
  • Computational cost for model fitting, validation, and testing
  • Cost of collecting features/attributes for test data
  • Cost of user feedback collection
  • Cost of incorrect prediction/classification

Cost-Sensitive Machine Learning is one of the first books to provide an overview of the current research efforts and problems in this area. It discusses real-world applications that incorporate the cost of learning into the modeling process.

 

The first part of the book presents the theoretical underpinnings of cost-sensitive machine learning. It describes well-established machine learning approaches for reducing data acquisition costs during training as well as approaches for reducing costs when systems must make predictions for new samples. The second part covers real-world applications that effectively trade off different types of costs. These applications not only use traditional machine learning approaches, but they also incorporate cutting-edge research that advances beyond the constraining assumptions by analyzing the application needs from first principles.

 

Spurring further research on several open problems, this volume highlights the often implicit assumptions in machine learning techniques that were not fully understood in the past. The book also illustrates the commercial importance of cost-sensitive machine learning through its coverage of the rapid application developments made by leading companies and academic research labs.

商品描述(中文翻譯)

在機器學習應用中,從業者必須考慮算法相關的成本。這些成本包括:

- 獲取訓練數據的成本
- 數據標註/標籤和清理的成本
- 模型擬合、驗證和測試的計算成本
- 收集測試數據的特徵/屬性的成本
- 用戶反饋收集的成本
- 錯誤預測/分類的成本

《成本敏感機器學習》是第一本概述該領域當前研究努力和問題的書籍之一。它討論了將學習成本納入建模過程的實際應用。

該書的第一部分介紹了成本敏感機器學習的理論基礎。它描述了減少訓練期間數據獲取成本的成熟機器學習方法,以及在系統必須對新樣本進行預測時減少成本的方法。第二部分涵蓋了有效平衡不同類型成本的實際應用。這些應用不僅使用傳統的機器學習方法,還通過從基本原理分析應用需求,融入了前沿研究。

這本書進一步推動了幾個開放問題的研究,突顯了過去對機器學習技術中常常隱含的假設的不充分理解。該書還通過介紹領先公司和學術研究實驗室的快速應用發展,展示了成本敏感機器學習的商業重要性。