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随机森林(RF)是一种统计学习理论,它是利用bootsrap重抽样方法从原始样本中抽取多个样本,对每个bootsrap样本进行决策树建模,然后组合多棵决策树的预测,通过投票得出最终预测结果。它具有很高的预测准确率,对异常值和噪声具有很好的容忍度,且不容易出现过拟合,在医学、生物信息、管理学等领域有着广泛的应用。为此,介绍了随机森林原理及其有关性质,讨论其最新的发展情况以及一些重要的应用领域。
Abstract：Random Forests is a statistical learning theory,using bootsrap re-sampling method form sample sets,and then combining the tree predictors by majority voting so that each tree is grown using a new bootstrap training set.It is widely applied in medicine,bioinformatics,economics and other fields,because of its high prediction accuracy,good tolerance of noisy data,and the law of large numbers they do not overfit.In this paper we first introduce the concept of random forest and the latest research,then provide some important aspects of applications in economics,and a summary is given in the final section.
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基本信息:
DOI：
中图分类号:O212.2
引用信息:
[1]方匡南,吴见彬,朱建平等.随机森林方法研究综述[J].统计与信息论坛,2011,26(03):32-38.
基金信息:
中央高校基本科研业务费专项资金《基于数据挖掘的数据质量管理研究》(2010221040);; 国家统计局重点项目《金融风险中的统计方法》(2009LZ045)