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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
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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

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  2. C. X. Ling and V. S. Sheng, Cost-Sensitive Learning and the Class Imbalance Problem.
  3. Sheng, V. S., & Ling, C. X. (2006). Thresholding for making classifiers cost-sensitive. AAAI’06: Proceedings of the 21st national conference on artificial intelligence, vol. 6, pp. 476–481.
  4. Pneumonia in Children Statistics – UNICEF data: https://data.unicef.org/topic/child-health/pneumonia/.
  5. 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.
  6. Machine Learning-Powered Search Ranking of Airbnb Experiences (2019...
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