Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions

TL;DR

This paper provides a comprehensive taxonomy and evaluation of oversampling algorithms tailored for imbalanced big data in distributed environments, specifically using Apache Spark, and offers guidelines for future algorithm design.

Contribution

It introduces a Spark library with 14 oversampling algorithms, evaluates their performance on large datasets, and formulates design guidelines for scalable imbalanced data handling.

Findings

Oversampling algorithms vary in effectiveness depending on classifier type.

Trade-offs exist between accuracy, time complexity, and scalability.

Guidelines for designing future oversampling methods for big data are proposed.

Abstract

Learning from imbalanced data is among the most challenging areas in contemporary machine learning. This becomes even more difficult when considered the context of big data that calls for dedicated architectures capable of high-performance processing. Apache Spark is a highly efficient and popular architecture, but it poses specific challenges for algorithms to be implemented for it. While oversampling algorithms are an effective way for handling class imbalance, they have not been designed for distributed environments. In this paper, we propose a holistic look on oversampling algorithms for imbalanced big data. We discuss the taxonomy of oversampling algorithms and their mechanisms used to handle skewed class distributions. We introduce a Spark library with 14 state-of-the-art oversampling algorithms implemented and evaluate their efficacy via extensive experimental study. Using binary and multi-class massive data sets, we analyze the effectiveness of oversampling algorithms and their relationships with different types of classifiers. We evaluate the trade-off between accuracy and time complexity of oversampling algorithms, as well as their scalability when increasing the size of data. This allows us to gain insight into the usefulness of specific components of oversampling algorithms for big data, as well as formulate guidelines and recommendations for designing future resampling approaches for massive imbalanced data. Our library can be downloaded from https://github.com/fsleeman/spark-class-balancing.git.