基于分类精度和相关性的随机森林算法改进研究

为了提升传统随机森林算法的分类精度,本文首先对传统随机森林模型中的决策树根据分类性能评价指标AUC值进行降序排列,从中选取出AUC值高的决策树,计算这些决策树之间的相似度并生成相似度矩阵,然后根据相似度矩阵对这些决策树进行聚类,从每一类中选出一棵AUC最大的决策树组成新的随机森林模型,从而达到提升传统随机森林算法分类精度的目的。通过UCI数据集的实验表明,改进后的随机森林算法分类精度上最大提高了2.91%。

Research on Improvement of Random Forests Algorithm Based on Classification Accuracy and Correlation

In order to improve the classification accuracy of random forests algorithm, the decision trees in the random forest model are first sorted according to the AUC value of the classification performance evaluation index. And then the trees with high AUC value is selected to calculate the similarity matrix. Finally the decision tree is clustered according to the similarity matrix. So a new random forest model is generated by selecting the tree with the highest AUC value from each category and to achieve the goal of improving the accuracy of random forests algorithm. Experiments on UCI datasets show that the improved random forest algorithm has improved the highest classification accuracy of 2.91%.