| 基于随机森林方法的异常样本检测方法 |
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| 引用本文： | 邱一卉,林成德.基于随机森林方法的异常样本检测方法[J].福建工程学院学报,2007,5(4):392-396. |
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| 作者姓名： | 邱一卉 林成德 |
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| 作者单位： | 厦门大学信息科学与技术学院自动化系,福建,厦门,361005 |
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| 摘 要： | 提出一种基于随机森林方法的异常样本(outliers)检测方法.仿真实验表明,与其他2种基于距离的异常样本检测技术相比,这种方法可以更好地提高模型的准确率,且具有较强的鲁棒性,在处理大规模数据集时还能显著地减少计算时间.
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| 关 键 词： | 异常样本检测 随机森林 马氏距离 |
| 文章编号： | 1672-4348（2007）04-0392-05 |
| 修稿时间： | 2007-06-20 |
Outlier detection based on random forest |
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| Qiu Yihui,Lin Chengde.Outlier detection based on random forest[J].Journal of Fujian University of Technology,2007,5(4):392-396. |
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| Authors: | Qiu Yihui Lin Chengde |
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| Institution: | Automation Department, Information Science and Technology School, Xiamen University, Xiamen 361005, China |
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| Abstract: | It introduces an outliers detection method based on random forest.Compared with the other two common outliers detection methods based on distance,the proposed method can improve the performance and robustness of the model and can also reduce the computation time. |
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| Keywords: | outlier detection random forest Mahalanobis distance |
| 本文献已被 CNKI 维普 万方数据 等数据库收录！ |
