Imbalanced Learning

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. 1.1 PROBLEM FORMULATION We start with the definition of imbalanced learning in this chapter to lay the foundation for further discussions in the book. Specifically, we define imbalanced learning as the learning process for data representation and information extraction with severe data distribution skews to develop effective decision boundaries to support the decision-making process. The learning process could involve supervised learning, unsupervised learning, semi-supervised learning, or a combination Imbalanced Learning: Foundations, Algorithms, and Applications, First Edition. Edited by Haibo He and Yunqian Ma. © 2013 The Institute of Electrical and Electronics Engineers, Inc. Published 2013 by John Wiley & Sons, Inc.