摘要
为了提升传统随机森林算法的分类精度,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(area under curve)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(university of Californialrvine)数据集的实验表明,改进后的随机森林算法在分类精度上最大提高了2.91%。
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%.
出处
《科学技术与工程》
北大核心
2017年第20期67-72,共6页
Science Technology and Engineering
基金
国家"863"计划(2014AA015204)
山西省国际科技合作项目(2014081018-2)资助
关键词
随机森林
分类精度
决策树相似度
相似度矩阵
random forest
classification accuracy
the similarity among decision trees
similarity matrix
