Cost-Sensitive Machine Learning
Cost-Sensitive Machine Learning
复制标题
成本敏感的机器学习
DOI：
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发表时间：
2011
期刊：
影响因子：
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通讯作者：
R. B. Rao
中科院分区：
文献类型：
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作者：
Balaji Krishnapuram;Shipeng Yu;R. B. Rao
在机器学习应用中，从业者必须考虑与算法相关的成本。这些费用包括：获取训练数据的成本数据注释/标记和清理的成本模型拟合、验证和测试的计算成本收集测试数据的特征/属性的成本用户反馈收集的成本错误预测/分类的成本成本成本敏感机器学习是第一本概述该领域当前研究工作和问题的书籍之一。它讨论了将学习成本纳入建模过程的实际应用。本书的第一部分介绍了成本敏感型机器学习的理论基础。它描述了成熟的机器学习方法，用于降低训练过程中的数据采集成本，以及当系统必须对新样本进行预测时降低成本的方法。第二部分涵盖了实际应用程序，有效地权衡不同类型的成本。这些应用程序不仅使用传统的机器学习方法，而且还结合了前沿研究，通过从第一原理分析应用程序需求，超越了限制性假设。本书对几个开放性问题进行了进一步的研究，突出了机器学习技术中通常隐含的假设，这些假设在过去没有得到充分的理解。本书还通过对领先公司和学术研究实验室的快速应用开发的报道，说明了成本敏感型机器学习的商业重要性。
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.