| 随机森林理论浅析 |
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| 引用本文： | 董师师,黄哲学. 随机森林理论浅析[J]. 集成技术, 2013, 2(1): 1-7 |
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| 作者姓名： | 董师师 黄哲学 |
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| 作者单位： | 深圳市高性能数据挖掘重点实验室;中国科学院深圳先进技术研究院 |
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| 摘 要： | 随机森林是一种著名的集成学习方法,被广泛应用于数据分类和非参数回归。本文对随机森林算法的主要理论进行阐述,包括随机森林收敛定理、泛化误差界以和袋外估计三个部分。最后介绍一种属性加权子空间抽样的随机森林改进算法,用于解决超高维数据的分类问题。
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| 关 键 词： | 随机森林 数据挖掘 机器学习 |
A Brief Theoretical Overview of Random Forests |
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| Dong Shishi,Huang Zhexue. A Brief Theoretical Overview of Random Forests[J]. , 2013, 2(1): 1-7 |
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| Authors: | Dong Shishi Huang Zhexue |
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| Affiliation: | 1,2 1( Shenzhen Key Laboratory of High Performance Data Mining, Shenzhen518055,China ) 2( Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen518055,China ) |
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| Abstract: | Random Forests is an important ensemble learning method and it is widely used in data classification and nonparametric regression. In this paper, we review three main theoretical issues of random forests, i.e., the convergence theorem, the generalization error bound and the out-of-bag estimation. In the end, we present an improved Random Forests algorithm, which uses a feature weighting sampling method to sample a subset of features at each node in growing trees. The new method is suitable to solve classification problems of very high dimensional data. |
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| Keywords: | random forests data mining machine learning |
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