Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity

HU Zhi-peng; Peng Yi-gong

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Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity

更新时间：2026-09-04

- Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity
- Vol. 35, Issue 7, Pages: 117-121(2018)
- 作者机构：
  
  华东理工大学信息科学与工程学院,上海,200237
- 作者简介：
- 基金信息：
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- Published：2018
- 稿件说明：
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胡志鹏, 彭亦功. 基于数据相似度的自适应半监督随机森林算法[J]. 微电子学与计算机, 2018, 35(7): 117-121.

HU Zhi-peng, Peng Yi-gong. Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity[J]. 2018, 35(7): 117-121.
胡志鹏, 彭亦功. 基于数据相似度的自适应半监督随机森林算法[J]. 微电子学与计算机, 2018, 35(7): 117-121. DOI：

HU Zhi-peng, Peng Yi-gong. Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity[J]. 2018, 35(7): 117-121. DOI：

摘要

提出一种基于数据相似度的自适应半监督随机森林算法.利用随机森林对带标签和无标记数据进行路径编码、相似度分析和无标签数据的伪标记选择; 再选择满足条件的数据迭代训练随机森林

改善其分类性能.实验结果表明:提出的算法可以有效地利用无标记数据信息

提高分类精度.

Abstract

An adaptive semi-supervised stochastic forest algorithm based on data similarity is proposed. The coding and mapping of labeled and unlabeled data are carried out by using random forest. The pseudo-marking of unlabeled data is selected. Then the randomized forest is trained to improve the classification performance. The experimental results show that the proposed algorithm can effectively use the unmarked data to improve the classification accuracy.

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references

Leistner C, Saffari A, Santner J, et al. Semi-Supervised Random Forests[C]// IEEE, International Conference on Computer Vision. IEEE, 2010: 506-513.

Liu X, Song M, Tao D, et al. Semi-supervised node splitting for random forest construction[C]//Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. 2013: 492-499.

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Sch?lkopf B, Platt J, Hofmann T. Fast Discriminative Visual Codebooks using Randomized Clustering Forests[C]// International Conference on Neural Information Processing Systems. MIT Press, 2006: 985-992.

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Geurts P, Ernst D, Wehenkel L. Extremely randomized trees[J]. Machine Learning, 2006, 63(1):3-42. DOI: 3

陶剑文, Fu-Lai CHUNG, 王士同, 等.稀疏标签传播:一种鲁棒的领域适应学习方法[J].软件学报, 2015, 26(5):977-1000. DOI: 977

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