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Cost-Sensitive Machine Learning (Paperback)
Loot Price: R1,788
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Cost-Sensitive Machine Learning (Paperback)
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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.
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