摘要
为了有效地减少样本训练时间,提高多类分类器的识别率,同时使模型具有较好的推广能力,在综合考虑待分类样本数和类别易分性能的基础上,在"先分样本数较大的类"和"先分易分的类"之间折衷考虑,提出一种基于样本的新的类划分方案.采用平衡决策树结构,得到了一种新的决策树支持向量机多类分类算法.实验结果表明,该算法在不降低识别率的情况下,能大大减少系统的训练时间,是一种有效的多类分类算法.
In order to decrease the sample training time effectively, improve the identification rate, and make the model has good generalization ability, a new class partition project based on samples is proposed. This project makes a comprehensive consideration of the number of waiting classification samples and the capability of class partition, and takes a compromise between the ＂first classifying the classes with a large number of samples＂ and the ＂first classifying the classes that can be partitioned easily＂. And a new decision-tree-based support vector machines multi-class classification algorithm is proposed, which adopts the balance decision tree structure. The experimental results show that the ＇algorithm can significantly reduce system training time at the condition of not reducing identification rate, and is an effective multi-class classification algorithm. ：
出处
《控制与决策》
EI
CSCD
北大核心
2011年第1期149-152,156,共5页
Control and Decision
基金
国家863计划项目(2007AA10Z237)
北京市自然科学基金项目(40810010)
关键词
支持向量机
决策树
多类分类器
类间可分性
support vector machines
decision tree
multi-class classifiers
inter-class separability
