摘要
利用Fayyad边界点判定原理对CART决策树选取连续属性的分割阈值的方法进行改进,由Fayyad边界点判定原理可知,建树过程中选取连续属性的分割阈值时,不需要检查每一个分割点,只要检查样本排序后,该属性相邻不同类别的分界点即可;针对样本集主类类属分布不平衡时,样本量占相对少数的小类属样本不能很好地对分类进行表决的情况,采用关键度度量的方法进行改进。基于这两点改进构建CART分类器。实验结果表明,Fayyad边界点判定原理适用于CART算法,利用改进后的CART算法生成决策树的效率提高了近45%,在样本集主类类属分布不平衡的情况下,分类准确率也略有提高。
Fayyad boundary point determination principle was used to improve the method of choosing continuous-valued attri-butes’segmentation threshold in CART decision tree.Through Fayyad boundary point determination principle,in the process of selecting continuous-valued attributes’segmentation threshold,adjacent boundary points which were sorted and in different clas-ses were checked,instead of getting every split point checked.And the key decision factor was used to improve the classification accuracy when the main classes of sample set distributed imbalanced.CART classifier was constructed based on these methods. The experimental result shows that Fayyad boundary point determination principle is appropriate for CART algorithm,the effi-ciency of building decision tree is improved by about 45 percent,and when the main classes of sample set distribute imbalanced, the classification accuracy of the improved algorithm is higher than that of the original one.
出处
《计算机工程与设计》
北大核心
2015年第5期1209-1213,共5页
Computer Engineering and Design
基金
国家973重点基础研究发展计划基金项目(2013CB328903-2)
关键词
决策树
CART算法
分割阈值
Fayyad边界点判定定理
关键度度量
decision tree CART algorithm
segmentation threshold
Fayyad boundary point determination principle
key deci-sion factor
