树形算法在电信客户细分中的应用研究  

Research on the application of tree algorithm in classification of telecom customers

作　　者：罗军[1] 张俊勇[1] 

机构地区：[1]重庆大学计算机学院,重庆400044

出　　处：《计算机时代》2014年第5期1-4,共4页Computer Era

摘　　要：树形算法由于其对大量高维数据的有效处理、对噪声点的高容忍度和对知识的有效表示,是最常用的CRM客户细分技术。通过对几类树形算法,包括决策树C4.5算法、决策树CART算法和平衡随机森林BRF算法,在解决电信客户细分问题中的表现进行分析研究,并且选用BP神经网络算法作为树形算法的参照,最终研究得出:平衡随机森林在处理电信客户问题上具有最好的表现。Due to the effective processing of large amounts of high-dimensional data, high tolerance for noise and effective representation of knowledge, tree algorithm is the most common CRM customer segmentation technique. The performance of tree algorithm, including the C4.5, the CART and the balanced random forest, in solving telecommunication customer segmentation problems is analyzed. BP neural network algorithm is compared. Experiments have shown that balanced random forest has the best performance in dealing with the problem.

关 键 词：决策树 随机森林 BP神经网络 数据预处理 

分 类 号：TP393[自动化与计算机技术—计算机应用技术]