基于数据相似度的自适应半监督随机森林算法

Adaptive Semi-Supervised Random Forest Algorithm Based on Data Similarity

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.

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.