Cost-Sensitive Machine Learning

Cost-Sensitive Machine Learning

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Features

  • Covers machine learning methods that minimize modeling and predictive costs
  • Presents the main theoretical approaches and implicit assumptions behind cost-sensitive machine learning
  • Identifies open problems for future research
  • Demonstrates the tradeoffs of costs and benefits in the data modeling processes of various applications, such as web ad placement, computer-aided medical diagnosis, computer vision, information extraction, and natural language processing

Summary

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.

Table of Contents

THEORECTICAL UNDERPINNINGS OF COST-SENSTIVE MACHINE LEARNING
Algorithms for Active Learning
, Burr Settles
Query Strategy Frameworks
A Unified View
Summary and Outlook

Semi-Supervised Learning: Some Recent Advances, Xueyuan Zhou, Ankan Saha, and Vikas Sindhwani
Semi-Supervised Prediction for Structured Outputs
Theoretical Analysis
New Directions

Transfer Learning, Multi-Task Learning, and Cost-Sensitive Learning, Bin Cao, Yu Zhang, and Qiang Yang
Notations
Transfer Learning Models
Multi-Task Learning Models
Conclusion and Future Work

Cost-Sensitive Cascades, Vikas C. Raykar
Features Incur a Cost
Cascade of Classifiers
Successful Applications of Cascaded Architectures
Training a Cascade of Classifiers
Tradeoff between Accuracy and Cost
Conclusions and Future Work

Selective Data Acquisition for Machine Learning, Josh Attenberg, Prem Melville, Foster Provost, and Maytal Saar-Tsechansky
Overarching Principles for Selective Data Acquisition
Active Feature-Value Acquisition
Labeling Features versus Examples
Dealing with Noisy Acquisition
Prediction Time Information Acquisition
Alternative Acquisition Settings
Conclusion

COST-SENSITIVE MACHINE LEARNING APPLICATIONS
Minimizing Annotation Costs in Visual Category Learning
, Sudheendra Vijayanarasimhan and Kristen Grauman
Reducing the Level of Supervision
Reducing the Amount of Supervision
Reducing the Effort Required in Supervision
Cost-Sensitive Multi-Level Active Learning
Conclusion

Reliability and Redundancy: Reducing Error Cost in Medical Imaging, X.S. Zhou, Y. Zhan, Z. Peng, M. Dewan, B. Jian, A. Krishnan, M. Harder, R. Schwarz, L. Lauer, H. Meyer, S. Grosskopf, U. Feuerlein, H. Ditt, and M. Scheuering
A Measure of Reliability
Reliability of Pattern Localization: Asymmetric Cost for FPs and FNs
Implications and Learning Strategy for Medical Imaging Applications
Related Work and Discussions

Cost-Sensitive Learning in Computational Advertising, Deepak Agarwal
Performance Advertising: Sponsored Search and Contextual Matching
Display Advertising
Discussion

Cost-Sensitive Machine Learning for Information Retrieval, Martin Szummer and Filip Radlinski
Utility in Information Retrieval
Learning to Rank
Reducing Labeling Cost
Multiple Utilities
Conclusion

Index

A Bibliography appears at the end of each chapter.

Editor Bio(s)

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.

 
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