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Machine Learning for Imbalanced Data

You're reading from   Machine Learning for Imbalanced Data Tackle imbalanced datasets using machine learning and deep learning techniques

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Product type Paperback
Published in Nov 2023
Publisher Packt
ISBN-13 9781801070836
Length 344 pages
Edition 1st Edition
Languages
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Authors (2):
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Dr. Mounir Abdelaziz Dr. Mounir Abdelaziz
Author Profile Icon Dr. Mounir Abdelaziz
Dr. Mounir Abdelaziz
Kumar Abhishek Kumar Abhishek
Author Profile Icon Kumar Abhishek
Kumar Abhishek
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Toc

Table of Contents (15) Chapters Close

Preface 1. Chapter 1: Introduction to Data Imbalance in Machine Learning FREE CHAPTER 2. Chapter 2: Oversampling Methods 3. Chapter 3: Undersampling Methods 4. Chapter 4: Ensemble Methods 5. Chapter 5: Cost-Sensitive Learning 6. Chapter 6: Data Imbalance in Deep Learning 7. Chapter 7: Data-Level Deep Learning Methods 8. Chapter 8: Algorithm-Level Deep Learning Techniques 9. Chapter 9: Hybrid Deep Learning Methods 10. Chapter 10: Model Calibration 11. Assessments 12. Index 13. Other Books You May Enjoy Appendix: Machine Learning Pipeline in Production

References

  1. X. He et al., “Practical Lessons from Predicting Clicks on Ads at Facebook,” in Proceedings of the Eighth International Workshop on Data Mining for Online Advertising, New York NY USA: ACM, Aug. 2014, pp. 1–9. doi: 10.1145/2648584.2648589.
  2. X. Ling, W. Deng, C. Gu, H. Zhou, C. Li, and F. Sun, “Model Ensemble for Click Prediction in Bing Search Ads,” in Proceedings of the 26th International Conference on World Wide Web Companion - WWW ’17 Companion, Perth, Australia: ACM Press, 2017, pp. 689–698. doi: 10.1145/3041021.3054192.
  3. How Uber Optimizes the Timing of Push Notifications using ML and Linear Programming: https://www.uber.com/blog/how-uber-optimizes-push-notifications-using-ml/.
  4. A. D. Pozzolo, O. Caelen, R. A. Johnson, and G. Bontempi, “Calibrating Probability with Undersampling for Unbalanced Classification,” in 2015 IEEE Symposium Series on Computational Intelligence, Cape Town, South Africa...
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