mlsanityData to 7 Oct 2026

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

arXiv 2107.11508 ↗First posted 24 Jul 2021cs.LGcited by 21

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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.

Key facts. Cited by 21 papers in the map (data through 7 Oct 2026). Builds on 31 of its 194 references that are in the map. First posted on arXiv 24 Jul 2021.

Builds on

31 of 194 references.

References · 31

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19 more
  1. 2018Multimodal Machine Learning for Automated ICD Codingcited by 40
  2. 2017A Survey on Multi-View Clusteringcited by 32
  3. 2019EmbraceNet: A robust deep learning architecture for multimodal classificationcited by 30
  4. 2017Temporal Multimodal Fusion for Video Emotion Classification in the Wildcited by 18
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  6. 2020Combined Cleaning and Resampling Algorithm for Multi-Class Imbalanced Data with Label Noisecited by 18
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Citing papers per year: 2022: 3; 2023: 6; 2024: 7; 2025: 2; 2026: 3.

21 papers.

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  4. 2024Reading Between the Frames: Multi-Modal Depression Detection in Videos from Non-Verbal Cuescited by 11
  5. 2024A Survey on Multimodal Wearable Sensor-based Human Action Recognitioncited by 10
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  10. 2023MuG: A Multimodal Classification Benchmark on Game Data with Tabular, Textual, and Visual Fieldscited by 5
  11. 2023MultiSChuBERT: Effective Multimodal Fusion for Scholarly Document Quality Predictioncited by 4
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  4. 2024Deep Learning-Based Noninvasive Screening of Type 2 Diabetes with Chest X-ray Images and Electronic Health Recordscited by 1
  5. 2026Artificial Intelligence for the Characterization of Particles and Fibers by Optical Microscopycited by 1
  6. 2023Explainable Multi-View Deep Networks Methodology for Experimental Physicscited by 0
  7. 2025Class Similarity-Based Multimodal Classification under Heterogeneous Category Setscited by 0
  8. 2026Residual-Guided Expert Specialization for Incomplete Multimodal Learningcited by 0
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