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

arXiv: https://arxiv.org/abs/2107.11508 · HTML: https://mlsanity.com/paper/2107.11508/

## Key facts

- Cited by 21 papers in the map (data through 14 Oct 2026).
- Builds on 31 of its 194 references that are in the map.
- First posted on arXiv 24 Jul 2021.

## Builds on (most cited first, 30 of 31)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Cost Sensitive Learning of Deep Feature Representations from Imbalanced Data](https://mlsanity.com/paper/1508.03422/) (2015)
- [A Survey on Multi-view Learning](https://mlsanity.com/paper/1304.5634/) (2013)
- [More Diverse Means Better: Multimodal Deep Learning Meets Remote Sensing Imagery Classification](https://mlsanity.com/paper/2008.05457/) (2020)
- [Deep Learning in Medical Image Registration: A Survey](https://mlsanity.com/paper/1903.02026/) (2019)
- [A Survey of Multi-View Representation Learning](https://mlsanity.com/paper/1610.01206/) (2016)
- [Multimodal Intelligence: Representation Learning, Information Fusion, and Applications](https://mlsanity.com/paper/1911.03977/) (2019)
- [Deep Learning for Classification of Hyperspectral Data: A Comparative Review](https://mlsanity.com/paper/1904.10674/) (2019)
- [Multimodal Prototypical Networks for Few-shot Learning](https://mlsanity.com/paper/2011.08899/) (2020)
- [Multimodal Machine Learning for Automated ICD Coding](https://mlsanity.com/paper/1810.13348/) (2018)
- [A Survey on Multi-View Clustering](https://mlsanity.com/paper/1712.06246/) (2017)
- [EmbraceNet: A robust deep learning architecture for multimodal classification](https://mlsanity.com/paper/1904.09078/) (2019)
- [Temporal Multimodal Fusion for Video Emotion Classification in the Wild](https://mlsanity.com/paper/1709.07200/) (2017)
- [A review on deep learning techniques for 3D sensed data classification](https://mlsanity.com/paper/1907.04444/) (2019)
- [Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noise](https://mlsanity.com/paper/2004.03406/) (2020)
- [AENet: Learning Deep Audio Features for Video Analysis](https://mlsanity.com/paper/1701.00599/) (2017)
- [Multimodal music information processing and retrieval: survey and future challenges](https://mlsanity.com/paper/1902.05347/) (2019)
- [Multimodal Emotion Classification](https://mlsanity.com/paper/1903.12520/) (2019)
- [ECG Heartbeat Classification Using Multimodal Fusion](https://mlsanity.com/paper/2107.09869/) (2021)
- [Multimodal deep learning approach for joint EEG-EMG data compression and classification](https://mlsanity.com/paper/1703.08970/) (2017)
- [A Joint Model for Multimodal Document Quality Assessment](https://mlsanity.com/paper/1901.01010/) (2019)
- [Multimodal Age and Gender Classification Using Ear and Profile Face Images](https://mlsanity.com/paper/1907.10081/) (2019)
- [Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning](https://mlsanity.com/paper/1908.08979/) (2019)
- [Multimodal Material Classification for Robots using Spectroscopy and High Resolution Texture Imaging](https://mlsanity.com/paper/2004.01160/) (2020)
- [Multimodal price prediction](https://mlsanity.com/paper/2007.05056/) (2020)
- [Hellinger Distance Weighted Ensemble for Imbalanced Data Stream Classification](https://mlsanity.com/paper/2102.00266/) (2021)
- [Multimodal Classification with Deep Convolutional-Recurrent Neural Networks for Electroencephalography](https://mlsanity.com/paper/1807.10641/) (2018)

## Built on by (most cited first, 21 of 21)

- [A review of deep learning-based information fusion techniques for multimodal medical image classification](https://mlsanity.com/paper/2404.15022/) (2024)
- [Does a Technique for Building Multimodal Representation Matter? -- Comparative Analysis](https://mlsanity.com/paper/2206.06367/) (2022)
- [Multimodal Data Integration for Oncology in the Era of Deep Neural Networks: A Review](https://mlsanity.com/paper/2303.06471/) (2023)
- [Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cues](https://mlsanity.com/paper/2401.02746/) (2024)
- [A Survey on Multimodal Wearable Sensor-based Human Action Recognition](https://mlsanity.com/paper/2404.15349/) (2024)
- [Enhancing Vision-Language Models with Scene Graphs for Traffic Accident Understanding](https://mlsanity.com/paper/2407.05910/) (2024)
- [Comprehensive Multimodal Deep Learning Survival Prediction Enabled by a Transformer Architecture: A Multicenter Study in Glioblastoma](https://mlsanity.com/paper/2405.12963/) (2024)
- [Improved Kidney Stone Recognition Through Attention and Multi-View Feature Fusion Strategies](https://mlsanity.com/paper/2211.02967/) (2022)
- [MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fields](https://mlsanity.com/paper/2302.02978/) (2023)
- [Improved Automatic Diabetic Retinopathy Severity Classification Using Deep Multimodal Fusion of UWF-CFP and OCTA Images](https://mlsanity.com/paper/2310.01912/) (2023)
- [MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Prediction](https://mlsanity.com/paper/2308.07971/) (2023)
- [Robust Multi-View Learning via Representation Fusion of Sample-Level Attention and Alignment of Simulated Perturbation](https://mlsanity.com/paper/2503.04151/) (2025)
- [Non-contact Multimodal Indoor Human Monitoring Systems: A Survey](https://mlsanity.com/paper/2312.07601/) (2023)
- [Embedding-based Multimodal Learning on Pan-Squamous Cell Carcinomas for Improved Survival Outcomes](https://mlsanity.com/paper/2406.08521/) (2024)
- [The Hateful Memes Challenge Next Move](https://mlsanity.com/paper/2212.06655/) (2022)
- [Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health Records](https://mlsanity.com/paper/2412.10955/) (2024)
- [Artificial Intelligence for the Characterization of Particles and Fibers by Optical Microscopy](https://mlsanity.com/paper/2608.00361/) (2026)
- [Explainable Multi-View Deep Networks Methodology for Experimental Physics](https://mlsanity.com/paper/2308.08206/) (2023)
- [Class Similarity-Based Multimodal Classification under Heterogeneous Category Sets](https://mlsanity.com/paper/2506.09745/) (2025)
- [Residual-Guided Expert Specialization for Incomplete Multimodal Learning](https://mlsanity.com/paper/2606.30355/) (2026)
- [Multimodal Cultural Heritage Architectural Style Classification for Residential Buildings in the UAE Based on CLIP Embeddings and SVM](https://mlsanity.com/paper/2609.17181/) (2026)

## Abstract (arXiv)

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.