Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search

• URL: http://arxiv.org/abs/2402.14389v1
• Date: Thu, 22 Feb 2024 09:01:42 GMT
• Title: Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search
• Authors: Md. Alamin Talukder, Rakib Hossen, Md Ashraf Uddin, Mohammed Nasir Uddin and Uzzal Kumar Acharjee
• Abstract summary: We introduce a state-of-the-art hybrid ensemble (ENS) Machine learning (ML) model that intelligently combines multiple algorithms to enhance fraud identification. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, and ENS models, respectively.
• Score: 2.4374097382908477
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Financial institutions and businesses face an ongoing challenge from fraudulent transactions, prompting the need for effective detection methods. Detecting credit card fraud is crucial for identifying and preventing unauthorized transactions.Timely detection of fraud enables investigators to take swift actions to mitigate further losses. However, the investigation process is often time-consuming, limiting the number of alerts that can be thoroughly examined each day. Therefore, the primary objective of a fraud detection model is to provide accurate alerts while minimizing false alarms and missed fraud cases. In this paper, we introduce a state-of-the-art hybrid ensemble (ENS) dependable Machine learning (ML) model that intelligently combines multiple algorithms with proper weighted optimization using Grid search, including Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), and Multilayer Perceptron (MLP), to enhance fraud identification. To address the data imbalance issue, we employ the Instant Hardness Threshold (IHT) technique in conjunction with Logistic Regression (LR), surpassing conventional approaches. Our experiments are conducted on a publicly available credit card dataset comprising 284,807 transactions. The proposed model achieves impressive accuracy rates of 99.66%, 99.73%, 98.56%, and 99.79%, and a perfect 100% for the DT, RF, KNN, MLP and ENS models, respectively. The hybrid ensemble model outperforms existing works, establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. The results highlight the effectiveness and reliability of our approach, demonstrating superior performance metrics and showcasing its exceptional potential for real-world fraud detection applications.

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