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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