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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

Index

As this ebook edition doesn't have fixed pagination, the page numbers below are hyperlinked for reference only, based on the printed edition of this book.

A

activation function 153

AdaBoost 103, 104

Adaptive Synthetic Sampling (ADASYN) 47

working 47-49

aggregating 92

algorithm-based techniques

deeper neural networks 240

regularization techniques 239

Siamese networks 239

threshold adjustment 240

AllKNN method 74-76

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oversampling techniques, in production 46

anomaly detection 27

artificial intelligence 4

AugMix function 192-194

average precision 18

B

backpropagation 155

bagging methods 101, 102

bagging techniques for imbalanced data 91-95

bagging methods 101, 102

OverBagging 97, 99

SMOTEBagging 99-101

UnderBagging 96, 97

balanced accuracy score 13

batch normalization 158

beta calibration 293

binary classification 264

boosting methods

performance comparison...

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