Hybrid Deep Learning Methods
In this chapter, we will talk about some of the hybrid deep learning techniques that combine the data-level (Chapter 7, Data-Level Deep Learning Methods) and algorithm-level (Chapter 8, Algorithm-Level Deep Learning Techniques) methods in some ways. This chapter contains some recent and more advanced techniques that can be challenging to implement, so it is recommended to have a good understanding of the previous chapters.
We will begin with an introduction to graph machine learning, clarifying how graph models exploit relationships within data to boost performance, especially for minority classes. Through a side-by-side comparison of a Graph Convolutional Network (GCN), XGBoost, and MLP models, using an imbalanced social network dataset, we will highlight the superior performance of the GCN.
We will continue to explore strategies to tackle class imbalance in deep learning, examining techniques that manipulate data distribution and prioritize challenging...
