skip to main content
US FlagAn official website of the United States government
dot gov icon
Official websites use .gov
A .gov website belongs to an official government organization in the United States.
https lock icon
Secure .gov websites use HTTPS
A lock ( lock ) or https:// means you've safely connected to the .gov website. Share sensitive information only on official, secure websites.

Explore Research Products in the PAR
It may take a few hours for recently added research products to appear in PAR search results.


This content will become publicly available on December 1, 2025

Title: Synthesizing class labels for highly imbalanced credit card fraud detection data
Acquiring labeled datasets often incurs substantial costs primarily due to the requirement of expert human intervention to produce accurate and reliable class labels. In the modern data landscape, an overwhelming proportion of newly generated data is unlabeled. This paradigm is especially evident in domains such as fraud detection and datasets for credit card fraud detection. These types of data have their own difficulties associated with being highly class imbalanced, which poses its own challenges to machine learning and classification. Our research addresses these challenges by extensively evaluating a novel methodology for synthesizing class labels for highly imbalanced credit card fraud data. The methodology uses an autoencoder as its underlying learner to effectively learn from dataset features to produce an error metric for use in creating new binary class labels. The methodology aims to automatically produce new labels with minimal expert input. These class labels are then used to train supervised classifiers for fraud detection. Our empirical results show that the synthesized labels are of high enough quality to produce classifiers that significantly outperform a baseline learner comparison when using area under the precision-recall curve (AUPRC). We also present results of varying levels of positive-labeled instances and their effect on classifier performance. Results show that AUPRC performance improves as more instances are labeled positive and belong to the minority class. Our methodology thereby effectively addresses the concerns of high class imbalance in machine learning by creating new and effective class labels.  more » « less
Award ID(s):
2231200
PAR ID:
10571787
Author(s) / Creator(s):
; ; ;
Editor(s):
Furht, Borko; Khoshgoftaar, Taghi
Publisher / Repository:
Springer Publisher
Date Published:
Journal Name:
Journal of Big Data
Volume:
11
Issue:
1
ISSN:
2196-1115
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
More Like this
  1. ; (, IEEE)
    Rubin, Stuart; Chen, Shu-Ching (Ed.)
    In this work, we use an unsupervised method for generating binary class labels in a novel context to create class labels for Medicare fraud detection. We examine how class imbalance influences the quality of these new labels and how it affects supervised classification. We use four different Medicare Part D fraud detection datasets, with the largest containing over 5 million instances. The other three datasets are sampled from the original dataset. Using Random Under-Sampling (RUS), we subsample from the majority class of the original data to produce three datasets with varying levels of class imbalance. To evaluate the performance of the newly created labels, we train a supervised classifier and evaluate its classification performance and compare it to an unsupervised anomaly detection method as a baseline. Our empirical findings indicate that the generated class labels are of high enough quality and enable effective supervised classifier training for fraud detection. Additionally, supervised classification with the new labels consistently outperforms the baseline used for comparison across all test scenarios. Further more, we observe an inverse relationship between class imbalance in the dataset and classifier performance, with AUPRC scores improving as the training dataset becomes more balanced. This work not only validates the efficacy of the synthesized class labels in labeling Medicare fraud but also shows its robustness across different degrees of class imbalance. 
    more » « less
  2. ; (, Machine Learning and Knowledge Extraction)
    The overall purpose of this paper is to demonstrate how data preprocessing, training size variation, and subsampling can dynamically change the performance metrics of imbalanced text classification. The methodology encompasses using two different supervised learning classification approaches of feature engineering and data preprocessing with the use of five machine learning classifiers, five imbalanced sampling techniques, specified intervals of training and subsampling sizes, statistical analysis using R and tidyverse on a dataset of 1000 portable document format files divided into five labels from the World Health Organization Coronavirus Research Downloadable Articles of COVID-19 papers and PubMed Central databases of non-COVID-19 papers for binary classification that affects the performance metrics of precision, recall, receiver operating characteristic area under the curve, and accuracy. One approach that involves labeling rows of sentences based on regular expressions significantly improved the performance of imbalanced sampling techniques verified by performing statistical analysis using a t-test documenting performance metrics of iterations versus another approach that automatically labels the sentences based on how the documents are organized into positive and negative classes. The study demonstrates the effectiveness of ML classifiers and sampling techniques in text classification datasets, with different performance levels and class imbalance issues observed in manual and automatic methods of data processing. 
    more » « less
  3. ; ; ; (, International Joint Conference on Neural Networks)
    As fraudulent activities have shot up manifolds, fraud detection has emerged as a pivotal process in different fields (e.g., e-commerce, online reviews, and social networks). Since interactions among entities provide valuable insights into fraudulent activities, such behaviors can be naturally represented as graph structures, where graph neural networks (GNNs) have been developed as prominent models to boost the efficacy of fraud detection. In graph-based fraud detection, handling imbalanced datasets poses a significant challenge, as the minority class often gets overshadowed, diminishing the performance of conventional GNNs. While oversampling has recently been adapted for imbalanced graphs, it contends with issues such as graph heterophily and noisy edge synthesis. To address these limitations, this paper introduces DOS-GNN, incorporating Dual-feature aggregation with Over-Sampling to advance GNNs for class-imbalanced fraud detection on graphs. This model exploits feature separation and dual-feature aggregation to mitigate the impact of heterophily and acquire refined node embeddings that facilitate fraud oversampling to balance class distribution without the need for edge synthesis. Extensive experiments on four large and real-world fraud datasets demonstrate that DOS-GNN can significantly improve fraud detection performance on graphs with different imbalance ratios and homophily ratios, outperforming state-of-the-art GNN models. 
    more » « less
  4. ; (, ICAIF '20: Proceedings of the First ACM International Conference on AI in Finance)
    Federated Learning enables a population of clients, working with a trusted server, to collaboratively learn a shared machine learning model while keeping each client's data within its own local systems. This reduces the risk of exposing sensitive data, but it is still possible to reverse engineer information about a client's private data set from communicated model parameters. Most federated learning systems therefore use differential privacy to introduce noise to the parameters. This adds uncertainty to any attempt to reveal private client data, but also reduces the accuracy of the shared model, limiting the useful scale of privacy-preserving noise. A system can further reduce the coordinating server's ability to recover private client information, without additional accuracy loss, by also including secure multiparty computation. An approach combining both techniques is especially relevant to financial firms as it allows new possibilities for collaborative learning without exposing sensitive client data. This could produce more accurate models for important tasks like optimal trade execution, credit origination, or fraud detection. The key contributions of this paper are: We present a privacy-preserving federated learning protocol to a non-specialist audience, demonstrate it using logistic regression on a real-world credit card fraud data set, and evaluate it using an open-source simulation platform which we have adapted for the development of federated learning systems. 
    more » « less
  5. ; ; ; (, Universe)
    Solar flares are characterized by sudden bursts of electromagnetic radiation from the Sun’s surface, and are caused by the changes in magnetic field states in active solar regions. Earth and its surrounding space environment can suffer from various negative impacts caused by solar flares, ranging from electronic communication disruption to radiation exposure-based health risks to astronauts. In this paper, we address the solar flare prediction problem from magnetic field parameter-based multivariate time series (MVTS) data using multiple state-of-the-art machine learning classifiers that include MINImally RandOm Convolutional KErnel Transform (MiniRocket), Support Vector Machine (SVM), Canonical Interval Forest (CIF), Multiple Representations Sequence Learner (Mr-SEQL), and a Long Short-Term Memory (LSTM)-based deep learning model. Our experiment is conducted on the Space Weather Analytics for Solar Flares (SWAN-SF) benchmark data set, which is a partitioned collection of MVTS data of active region magnetic field parameters spanning over nine years of operation of the Solar Dynamics Observatory (SDO). The MVTS instances of the SWAN-SF dataset are labeled by GOES X-ray flux-based flare class labels, and attributed to extreme class imbalance because of the rarity of the major flaring events (e.g., X and M). As a performance validation metric in this class-imbalanced dataset, we used the True Skill Statistic (TSS) score. Finally, we demonstrate the advantages of the MVTS learning algorithm MiniRocket, which outperformed the aforementioned classifiers without the need for essential data preprocessing steps such as normalization, statistical summarization, and class imbalance handling heuristics. 
    more » « less
  • Citation Formats
  • MLA
  • APA
  • Chicago
  • BibTeX
  • Save / Share this Record
  • Send to Email