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Aggregates Classification

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Related Experiment Video

Updated: Jan 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Enhancing credit card fraud detection using DBSCAN-augmented disjunctive voting ensemble.

Mahmoud A Ghalwash1,2, Samir Mohamed Abdelrazek1, Nabila Hamid Eladawi3

  • 1Faculty of Computers and Information, Mansoura University, Mansoura, Egypt.

Scientific Reports
|November 13, 2025
PubMed
Summary

This study introduces a hybrid framework using density-based clustering for data augmentation and an ensemble classifier to improve credit card fraud detection. The novel approach significantly enhances recall, achieving up to 99.5% for detecting rare fraudulent transactions.

Keywords:
Credit card fraud detectionData augmentationData imbalanceDisjunctive voting ensembleEnsemble learningHybrid ensemble approachMachine learning

Related Experiment Videos

Last Updated: Jan 11, 2026

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances
07:35

Selecting Multiple Biomarker Subsets with Similarly Effective Binary Classification Performances

Published on: October 11, 2018

7.9K

Area of Science:

  • Machine Learning
  • Data Science
  • Cybersecurity

Background:

  • Credit card fraud detection faces challenges due to extreme class imbalance, where fraudulent transactions are rare.
  • Existing methods struggle to effectively identify minority fraud instances, leading to significant financial losses.

Purpose of the Study:

  • To propose a novel hybrid framework for enhanced credit card fraud detection.
  • To address the class imbalance problem and improve the recall of fraud detection systems.

Main Methods:

  • Utilized density-based spatial clustering of applications with noise (DBSCAN) for data augmentation of the minority fraud class.
  • Developed an ensemble classification model using Random Forest (RF), K-Nearest Neighbors (KNN), and Support Vector Machine (SVM).
  • Implemented a disjunctive voting ensemble (DVE) strategy to prioritize high recall and minimize false negatives.

Main Results:

  • DBSCAN-based augmentation effectively increased minority-class representation while preserving fraud patterns.
  • The DVE strategy achieved high recall (up to 99.5%) and F1-scores (up to 99.8%).
  • The framework demonstrated superior performance over traditional methods, achieving 100% accuracy and precision in experiments.

Conclusions:

  • The proposed hybrid framework offers a robust, scalable, and interpretable solution for credit card fraud detection.
  • DBSCAN augmentation and DVE ensemble significantly improve the detection of rare fraudulent transactions.
  • This advancement contributes to developing more adaptive and effective fraud detection systems for real-world financial transactions.