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Machine Learning for Imbalanced Data

You're reading from  Machine Learning for Imbalanced Data

Product type Book
Published in Nov 2023
Publisher Packt
ISBN-13 9781801070836
Pages 344 pages
Edition 1st Edition
Languages
Authors (2):
Kumar Abhishek Kumar Abhishek
Profile icon Kumar Abhishek
Dr. Mounir Abdelaziz Dr. Mounir Abdelaziz
Profile icon Dr. Mounir Abdelaziz
View More author details

Table of Contents (15) Chapters

Preface 1. Chapter 1: Introduction to Data Imbalance in Machine Learning 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. C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On Calibration of Modern Neural Networks.” arXiv, Aug. 03, 2017. Accessed: Nov. 21, 2022, http://arxiv.org/abs/1706.04599
  2. A. Niculescu-Mizil and R. Caruana, “Predicting good probabilities with supervised learning,” in Proceedings of the 22nd International Conference on Machine Learning - ICML ‘05, Bonn, Germany, 2005, pp. 625–632. doi: 10.1145/1102351.1102430.
  3. J. Mukhoti, V. Kulharia, A. Sanyal, S. Golodetz, P. H. S. Torr, and P. K. Dokania, “Calibrating Deep Neural Networks using Focal Loss”. Feb 2020, https://doi.org/10.48550/arXiv.2002.09437
  4. B. C. Wallace and I. J. Dahabreh, “Class Probability Estimates are Unreliable for Imbalanced Data (and How to Fix Them),” in 2012 IEEE 12th International Conference on Data Mining, Brussels, Belgium, Dec. 2012, pp. 695–704. doi: 10.1109/ICDM.2012.115.
  5. M. Pakdaman Naeini, G. Cooper, and...
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