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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

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  2. T. Fawcett, An introduction to ROC analysis, Pattern Recognition Letters, vol. 27, no. 8, pp. 861–874, Jun. 2006, doi: 10.1016/j.patrec.2005.10.010.
  3. Y.-A. Le Borgne, W. Siblini, B. Lebichot, and G. Bontempi, Reproducible Machine Learning for Credit Card Fraud Detection - Practical Handbook. Université Libre de Bruxelles, 2022. [Online]. Available at https://github.com/Fraud-Detection-Handbook/fraud-detection-handbook.
  4. W. Siblini, J. Fréry, L. He-Guelton, F. Oblé, and Y.-Q. Wang, Master your Metrics with Calibration, vol. 12080, 2020, pp. 457–469. doi: 10.1007/978-3-030-44584-3_36.
  5. Xu-Ying Liu, Jianxin Wu, and Zhi-Hua Zhou, Exploratory Undersampling for Class-Imbalance Learning, IEEE Trans. Syst., Man, Cybern. B, vol. 39, no. 2, pp. 539–550, Apr. 2009, doi: 10.1109/TSMCB.2008.2007853.
  6. M. S. Santos, J. P. Soares, P. H. Abreu, H. Araujo, and J. Santos, Cross-Validation for Imbalanced Datasets: Avoiding Overoptimistic and Overfitting Approaches [Research Frontier], IEEE Comput. Intell. Mag., vol. 13, no. 4, pp. 59–76, Nov. 2018, doi: 10.1109/MCI.2018.2866730.
  7. A. Fernández, S. García, M. Galar, R. Prati, B. Krawczyk, and F. Herrera, Learning from Imbalanced Data Sets. Springer International Publishing, 2018
You have been reading a chapter from
Machine Learning for Imbalanced Data
Published in: Nov 2023
Publisher: Packt
ISBN-13: 9781801070836
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