Summary of Securing Transactions: a Hybrid Dependable Ensemble Machine Learning Model Using Iht-lr and Grid Search, by Md. Alamin Talukder et al.
Securing Transactions: A Hybrid Dependable Ensemble Machine Learning Model using IHT-LR and Grid Search
by Md. Alamin Talukder, Rakib Hossen, Md Ashraf Uddin, Mohammed Nasir Uddin, Uzzal Kumar Acharjee
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: General Finance (q-fin.GN)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper presents a state-of-the-art hybrid ensemble machine learning model that combines multiple algorithms to detect credit card fraud with high accuracy while minimizing false alarms. The proposed model, which includes decision trees, random forests, K-nearest neighbors, and multilayer perceptrons, is trained on a publicly available credit card dataset comprising 284,807 transactions. By employing the Instant Hardness Threshold technique in conjunction with logistic regression, the model surpasses conventional approaches. The results show impressive accuracy rates of up to 99.79% for the hybrid ensemble model, outperforming existing works and establishing a new benchmark for detecting fraudulent transactions in high-frequency scenarios. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about finding ways to stop credit card fraud from happening. It’s important because if we can catch these fake transactions quickly, it will help prevent people from losing money. The problem is that it takes a long time to check all the possible fake transactions, so we need a way to make it faster and more accurate. This paper presents a new way of using different machine learning models together to find fake transactions. It uses data from over 284,000 real credit card transactions to test its approach and shows that it can be very good at finding fake transactions without sending too many false alarms. |
Keywords
* Artificial intelligence * Ensemble model * Logistic regression * Machine learning
