引入了一种能较好容忍噪声,且稳定性较高的组合分类器算法———随机森林(RF),建立企业信用评估模型;着重分析了适合RF的不平衡分类问题的处理方法,并介绍了模型参数的优化.通过与神经网络和支持向量机的对比实验,证实了该方法的有效性和优越性.We introduce a new classifier combination algorithm——random forests(RF),which is rather stable and robust with noise.By analyzing the real data,a new model based on RF is built and tested.Empirical results show that the new proposed model is effective and more advantageous than those of both neural network model and SVM model
