# Ensemble and Mixed Learning Techniques for Credit Card Fraud Detection

arXiv: https://arxiv.org/abs/2112.02627 · HTML: https://mlsanity.com/paper/2112.02627/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 2 of its 48 references that are in the map.
- First posted on arXiv 5 Dec 2021.

## Builds on (most cited first, 2 of 2)

- [On Hyperparameter Optimization of Machine Learning Algorithms: Theory and Practice](https://mlsanity.com/paper/2007.15745/) (2020)
- [Optimal Algorithm for Profiling Dynamic Arrays with Finite Values](https://mlsanity.com/paper/1812.05306/) (2018)

## Abstract (arXiv)

Spurious credit card transactions are a significant source of financial losses and urge the development of accurate fraud detection algorithms. In this paper, we use machine learning strategies for such an aim. First, we apply a mixed learning technique that uses K-means preprocessing before trained classification to the problem at hand. Next, we introduce an adapted detector ensemble technique that uses OR-logic algorithm aggregation to enhance the detection rate. Then, both strategies are deployed in tandem in numerical simulations using real-world transactions data. We observed from simulation results that the proposed methods diminished computational cost and enhanced performance concerning state-of-the-art techniques.