Cost-Sensitive Machine Learning
暫譯: 成本敏感的機器學習

Krishnapuram, Balaji, Yu, Shipeng, Rao, R. Bharat

Cost-Sensitive Machine Learning

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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，實踐者必須考慮與算法相關的成本。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
- 收集測試數據特徵/屬性的成本
- 收集用戶反饋的成本
- 錯誤預測/分類的成本

成本敏感機器學習是第一本提供該領域當前研究努力和問題概述的書籍之一。它討論了將學習成本納入建模過程的現實應用。

本書的第一部分介紹了成本敏感機器學習的理論基礎。它描述了在訓練過程中減少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權衡不同類型成本的現實應用。這些應用不僅使用傳統的機器學習方法，還結合了超越約束假設的前沿研究，從基本原則分析應用需求。

本書促進了對幾個未解決問題的進一步研究，突顯了過去未被充分理解的機器學習技術中常常隱含的假設。該書還通過涵蓋領先公司和學術研究實驗室所做的快速應用開發，展示了成本敏感機器學習的商業重要性。

作者簡介

Balaji Krishnapuram is a senior R&D manager at Siemens Medical Solutions. He earned a Ph.D. in electrical and computer engineering from Duke University. His research interests include statistical data mining and information retrieval.



Shipeng Yu is a senior staff scientist at Siemens Medical Solutions. He earned a Ph.D. in computer science from the University of Munich. His research interests include statistical machine learning, data mining, Bayesian analysis, information retrieval and extraction, healthcare analytics, and personalized medicine.



R. Bharat Rao is senior director and head of Knowledge Solutions at Siemens Medical Solutions, where was recognized as one of its Inventors of the Year in 2005. He also received the 2011 ACM SIGKDD Lifetime Service Award for pioneering applications of data mining for healthcare. He earned a Ph.D. in electrical and computer engineering from the University of Illinois at Urbana-Champaign. His research interests include machine learning, healthcare analytics, mining large data, and personalized medicine.

作者簡介(中文翻譯)

Balaji Krishnapuram 是西門子醫療解決方案的高級研發經理。他在杜克大學獲得電機與計算機工程的博士學位。他的研究興趣包括統計數據挖掘和信息檢索。



Shipeng Yu 是西門子醫療解決方案的高級科學家。他在慕尼黑大學獲得計算機科學的博士學位。他的研究興趣包括統計機器學習、數據挖掘、貝葉斯分析、信息檢索與提取、醫療分析和個性化醫療。



R. Bharat Rao 是西門子醫療解決方案的高級總監及知識解決方案部門負責人，並於2005年被評選為年度發明家之一。他還因為在醫療領域開創數據挖掘應用而獲得2011年ACM SIGKDD終身服務獎。他在伊利諾伊大學香檳分校獲得電機與計算機工程的博士學位。他的研究興趣包括機器學習、醫療分析、大數據挖掘和個性化醫療。

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