Learning from Imbalanced Data

With the continuous expansion of data availability in many large-scale, complex, and networked systems, such as surveillance, security, Internet, and finance, it becomes critical to advance the fundamental understanding of knowledge discovery and analysis from raw data to support decision-making processes. Although existing knowledge discovery and data engineering techniques have shown great success in many real-world applications, the problem of learning from imbalanced data (the imbalanced learning problem) is a relatively new challenge that has attracted growing attention from both academia and industry. The imbalanced learning problem is concerned with the performance of learning algorithms in the presence of underrepresented data and severe class distribution skews. Due to the inherent complex characteristics of imbalanced data sets, learning from such data requires new understandings, principles, algorithms, and tools to transform vast amounts of raw data efficiently into information and knowledge representation. In this paper, we provide a comprehensive review of the development of research in learning from imbalanced data. Our focus is to provide a critical review of the nature of the problem, the state-of-the-art technologies, and the current assessment metrics used to evaluate learning performance under the imbalanced learning scenario. Furthermore, in order to stimulate future research in this field, we also highlight the major opportunities and challenges, as well as potential important research directions for learning from imbalanced data.

1 Introduction

Recent developments in science and technology have enabled the growth and availability of raw data to occur at an explosive rate. This has created an immense opportunity for knowledge discovery and data engineering research to play an essential role in a wide range of applications from daily civilian life to national security, from enterprise information processing to governmental decision-making support systems, from microscale data analysis to macroscale knowledge discovery. In recent years, the imbalanced learning problem has drawn a significant amount of interest from academia, industry, and government funding agencies. The fundamental issue with the imbalanced learning problem is the ability of imbalanced data to significantly compromise the performance of most standard learning algorithms. Most standard algorithms assume or expect balanced class distributions or equal misclassification costs. Therefore, when presented with complex imbalanced data sets, these algorithms fail to properly represent the distributive characteristics of the data and resultantly provide unfavorable accuracies across the classes of the data. When translated to real-world domains, the imbalanced learning problem represents a recurring problem of high importance with wide-ranging implications, warranting increasing exploration. This increased interest is reflected in the recent installment of several major workshops, conferences, and special issues including the American Association for Artificial Intelligence (now the Association for the Advancement of Artificial Intelligence) workshop on Learning from Imbalanced Data Sets (AAAI ’00) [1], the International Conference on Machine Learning workshop on Learning from Imbalanced Data Sets (ICML’03) [2], and the Association for Computing Machinery Special Interest Group on Knowledge Discovery and Data Mining Explorations (ACM SIGKDD Explorations ’04) [3].