基于MapReduce的供应链大数据金融信用评估模型

Financial Credit Assessment Model of a Supply Chain Big Data Based on MapReduce

针对供应链金融信用评估中传统单机环境和简单机器学习预测分析的高效性和精准性问题,研究了一种并行的集成学习评估模型,阐述了模型基于MapReduce并行的编程模型,集成多个随机森林,通过建模和预测实现信用评估的理论和技术实现原理。分析了模型的供应链金融信用评估性能,得出该模型能为大数据环境下的供应链金融信用评估提供可行参考的结论。

As to the predicting efficiency and accuracy in traditional single-machine environment and simple machinelearning methods while applied to supply chain financial credit assessment,a parallel ensemble learning assessmentmodel is studied.This paper elaborates the principle of MapReduce parallel programming method which integratesmultiple random forests to implement credit assessment by modeling and predicting.And it analyzes the performance ofproposed model while applied to supply chain financial credit assessment,and comes to the conclusion that proposedmodel could provide a viable reference method for achieving parallel credit assessment in the circumstances of big data.