Introduction
Document Type
Article
Date of Original Version
1-1-2013
Abstract
With the continuous expansion of data availability in many large-scale, complex, and networked systems, it becomes critical to advance raw data from fundamental research on the Big Data challenge to support decision-making processes. Although existing machine-learning and data-mining techniques have shown great success in many real-world applications, learning from imbalanced data is a relatively new challenge. This book is dedicated to the state-of-the-art research on imbalanced learning, with a broader discussions on the imbalanced learning foundations, algorithms, databases, assessment metrics, and applications. In this chapter, we provide an introduction to problem formulation, a brief summary of the major categories of imbalanced learning methods, and an overview of the challenges and opportunities in this field. This chapter lays the structural foundation of this book and directs readers to the interesting topics discussed in subsequent chapters.
Publication Title, e.g., Journal
Imbalanced Learning Foundations Algorithms and Applications
Citation/Publisher Attribution
He, Haibo. "Introduction." Imbalanced Learning Foundations Algorithms and Applications (2013). doi: 10.1002/9781118646106.ch1.