决策森林研究综述  

Review of decision forest

作　　者：黄海新[1] 吴迪[2] 文峰[1] 

机构地区：[1]沈阳理工大学信息科学与工程学院,辽宁沈阳110159 [2]沈阳理工大学自动化与电气工程学院,辽宁沈阳110159

出　　处：《电子技术应用》2016年第12期5-9,共5页Application of Electronic Technique

摘　　要：随着经济与社会的发展,数据挖掘技术广泛应用到各个领域,其中分类算法中的决策森林(Decision Forest)成为一个研究热点。决策森林算法是一种包含多个决策树分类器的统计学习理论,能较好地处理噪声且避免发生过拟合。针对几种典型的决策森林算法,阐述了其原理和算法的特点,并从决策森林的构建过程出发,系统地分析和总结了国内外现有的决策森林算法。在此基础上,详细说明了在面对大数据时应用决策森林进行分布式计算的处理过程。通过比较,总结出了各种决策森林算法的适用范围。With the development of economy and society, the Data Mining Technology runs through all areas widely. In which the forest decision of the classification algorithm has become a hot topic.Decision forest algorithm is statistics theory that combins the set of decision tree classification, can deal with noise and avoid over fitting surpassingly. This article mainly introduced the several classic methods of decision forest algorithm and their characteristics. Researching algorithms in domestic and overseas were analyzed and summarized systematically from the process of the construction of the decision forest. In the face of big data is described in detail application decision forest distributed computing process. By comparison,this article summarizes the applicable scope of various decision forest algorithm.

关 键 词：数据挖掘 抽样 决策森林 分类 分布式计算 决策树 

分 类 号：TP301[自动化与计算机技术—计算机系统结构]