INTRODUCTION - Imbalanced Learning: Foundations, Algorithms, and Applications - page 1

INTRODUCTION

HAIBO HE
Department of Electrical, Computer, and Biomedical Engineering, University of Rhode
Island, Kingston, RI, USA

Abstract: With the continuous expansion of data availability in many large-scale,
complex, and networked systems, it becomes critical to advance raw data from fun-
damental 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 rel-
atively new challenge. This topic is dedicated to the state-of-the-art research on
imbalanced learning, with a broader discussions on the imbalanced learning foun-
dations, 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 topic
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 topic. 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 super-
vised learning, unsupervised learning, semi-supervised learning, or a combination