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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

Concepts

Authors (2):

Kumar Abhishek

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

Why subscribe?

13. Other Books You May Enjoy

Appendix: Machine Learning Pipeline in Production

References

Fraud Detection: Using Relational Graph Learning to Detect Collusion (2021): https://www.uber.com/blog/fraud-detection/.
Graph for fraud detection (2022): https://engineering.grab.com/graph-for-fraud-detection.
A. Shrivastava, A. Gupta, and R. Girshick, “Training Region-Based Object Detectors with Online Hard Example Mining,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, NV, USA, Jun. 2016, pp. 761–769: doi: 10.1109/CVPR.2016.89.
Marius Schmidt-Mengin, Théodore Soulier, Mariem Hamzaoui, Arya Yazdan-Panah, Benedetta Bod-ini, et al. “Online hard example mining vs. fixed oversampling strategy for segmentation of new multiple sclerosis lesions from longitudinal FLAIR MRI”. Frontiers in Neuroscience, 2022, 16, pp.100405. 10.3389/fnins.2022.1004050. hal-03836922.
Q. Dong, S. Gong, and X. Zhu, “Class Rectification Hard Mining for Imbalanced Deep Learning,” in 2017...

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Authors (2)

Kumar Abhishek

Kumar Abhishek is a seasoned Senior Machine Learning Engineer at Expedia Group, US, specializing in risk analysis and fraud detection for Expedia brands. With over a decade of experience at companies such as Microsoft, Amazon, and a Bay Area startup, Kumar holds an MS in Computer Science from the University of Florida.

See other products by Kumar Abhishek

Dr. Mounir Abdelaziz

Dr. Mounir Abdelaziz is a deep learning researcher specializing in computer vision applications. He holds a Ph.D. in computer science and technology from Central South University, China. During his Ph.D. journey, he developed innovative algorithms to address practical computer vision challenges. He has also authored numerous research articles in the field of few-shot learning for image classification.

See other products by Dr. Mounir Abdelaziz

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