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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

Algorithm-Level Deep Learning Techniques

The data-level deep learning techniques have problems very similar to classical ML techniques. Since deep learning algorithms are quite different from classical ML techniques, we’ll explore some algorithm-level techniques for addressing data imbalance in this chapter. These algorithm-level techniques won’t change the data but accommodate the model instead. This exploration might uncover new insights or methods to better handle imbalanced data.

This chapter will be on the same lines as Chapter 5, Cost-Sensitive Learning, extending the ideas to deep learning models. We will look at algorithm-level deep learning techniques to handle the imbalance in data. Generally, these techniques do not modify the training data and often require no pre-processing steps, offering the benefit of no increased training times or additional runtime hardware costs.

In this chapter, we will cover the following topics:

  • Motivation for algorithm...
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