基于随机森林算法的B2B客户分级系统的设计

摘要

对分类数据挖掘算法进行研究，发现随机森林算法精度高、训练速度快、支持在线学习，因此提出在系统中使用该算法。针对随机森林算法抗噪声能力一般的问题，采用Bagging方法随机选择几组历史客户分级数据作为算法的训练数据，通过随机森林算法训练出分级模型，并通过这个模型对新客户数据进行自动分级。%The classification data mining algorithm is studied in this paper. The random forest algorithm has the advantages of high precision,fast training speed and supporting online learning,which is applied in classification system. Since random forest algorithm has general noise resisted ability,several groups classification data of history client are selected by using Bagging method randomly as the algorithm′s training data. The classification model is obtained by random forest algorithm training. New client data are classified automatically by using this model.