Proceedings ArticleDOI
An Improved XGBoost Model Based on Spark for Credit Card Fraud Prediction
Hongwei Chen,He Ai,Zhihui Yang,Weiwei Yang,Zhiwei Ye,Dawei Dong +5 more
- pp 1-6
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TLDR
In this article, an improved XGBoost model based on Spark is proposed to detect credit card fraud and the experimental results show that the proposed model can accurately and efficiently predict credit card Fraud and has a good practical effect.Abstract:
Credit card fraud causes huge economic losses for many financial institutions. Given the imbalance of dataset and the huge amount of data in the field of credit card fraud, an improved XGBoost model based on Spark is proposed. In this project, the Smote algorithm was used to to balance the training set. And the XGBoost classifier based on Spark was used as the fraud detection mechanism. Finally, the test sets were classified in parallel. In the model comparison experiment, the model proposed in this project is compared with logistic regression model, decision tree model, random forest model, and original XGBoost model. The experimental results show that in the three metrics of Recall, Fl-Score, and AUC, the model proposed in this project is the best, which is 9.1%, 1.4%, and 1.2% ahead of the model ranked second respectively. In the speedup experiment, the speedup on the dataset of 70,000, 140,000, and 280,000 samples are 2.06, 3.28, and 3.75 respectively. The experimental results of these two parts show that the proposed model can accurately and efficiently predict credit card fraud and has a good practical effect.read more
Citations
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Journal ArticleDOI
Credit card fraud detection in the era of disruptive technologies: A systematic review
TL;DR: In this paper , the authors present an in-depth review of cutting-edge research on detecting and predicting fraudulent credit card transactions conducted from 2015 to 2021 inclusive, and highlight the challenges associated with detecting credit card fraud through the use of new technologies such as big data analytics, large scale machine learning and cloud computing.
Proceedings ArticleDOI
Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE
TL;DR: In this article , an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework was used for air quality classification in Indonesia's major cities, where the overall accuracy value of SMOTE with XGBoost is 99.60%, the overall recall value was 99.6% and the overall f1-score is 99 .96%.
Proceedings ArticleDOI
Air Quality Prediction Based on Air Pollution Emissions in the City Environment Using XGBoost with SMOTE
TL;DR: In this article , an ensemble machine learning approach built on a decision tree and applying a gradient reinforcement framework was used for air quality classification in Indonesia's major cities, where the overall accuracy value of SMOTE with XGBoost is 99.60%, the overall recall value was 99.6% and the overall f1-score is 99 .96%.
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