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Updated: Feb 1, 2026

Rare Event Detection Using Error-corrected DNA and RNA Sequencing
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Rare event detection by progressive clustering undersampling.

Amr Abuzeid1, Elena Jolkver1

  • 1Data Science Dept/IU Internationale Hochschule GmbH, Juri-Gagarin-Ring, Erfurt, Germany.

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Summary

This study introduces Progressive Clustering Undersampling (PCU) to effectively capture rare events in imbalanced datasets. PCU outperforms other methods in identifying anomalies, offering a promising solution for complex data challenges.

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Area of Science:

  • Machine Learning
  • Data Science
  • Artificial Intelligence

Background:

  • Imbalanced datasets pose challenges for machine learning models, leading to bias towards majority classes.
  • Accurate identification of rare events and anomalies is crucial in various fields.

Purpose of the Study:

  • To address the challenge of capturing rare events in imbalanced datasets.
  • To introduce and evaluate a novel undersampling technique, Progressive Clustering Undersampling (PCU).

Main Methods:

  • Exploration of various resampling techniques for imbalanced data.
  • Development and implementation of the Progressive Clustering Undersampling (PCU) method.
  • Comparison of PCU against eight undersampling and two oversampling techniques.

Main Results:

  • PCU consistently outperformed existing methods on highly imbalanced and noisy datasets.
  • The workflow demonstrated effective rare anomaly prediction using unsupervised methods.
  • Progressive clustering successfully identified clusters with high concentrations of positive instances.

Conclusions:

  • The proposed PCU method offers a promising solution for identifying rare anomalies in complex, imbalanced data environments.
  • The approach enables effective prediction of rare anomalies through frequency-driven decision boundaries and clustering.
  • The method yields two optimized outputs for high F1-score and high precision.