XGboost and random forest algorithm for supply fraud forecasting

Guangzhong Pan

doi:10.1117/12.2641948

10 November 2022 XGboost and random forest algorithm for supply fraud forecasting

Guangzhong Pan

Proceedings Volume 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022); 123481G (2022) https://doi.org/10.1117/12.2641948
Event: 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 2022, Zhuhai, China

Abstract

With the comprehensive development and application of machine learning, forecasting supply by machine learning has achieved good results. However, as supply chain fraud is more complicated, using a simple machine learning algorithm can no longer achieve better performance for supply fraud forecasting. In this paper, we propose an XGboost and random forest algorithm to predict supply fraud. This algorithm first uses the random forest to filter out the unimportant variables, get essential variables, and then build an XGBoost model to predict supply chain fraud. Experimental results demonstrate that our proposed XGboost and random forest algorithm achieves great efficiency for supply fraud prediction than logistic, random forest, and XGBoost algorithms.

Citation Download Citation

Guangzhong Pan "XGboost and random forest algorithm for supply fraud forecasting", Proc. SPIE 12348, 2nd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2022), 123481G (10 November 2022); https://doi.org/10.1117/12.2641948

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