计算机应用与软件Issue(12)：233-236,4.DOI:10.3969/j.issn.1000-386x.2014.12.056
一种改进的基于支持向量机的多类分类方法
AN IMPROVED SVM-BASED MULTI-CLASS CLASSIFICATION ALGORITHM
赵亮1
作者信息
- 1. 重庆邮电大学计算机科学与技术学院 重庆400065
- 折叠
摘要
Abstract
In light of the deficiency of existing SVM multi-class classification algorithm in classification accuracy, we propose an improved SVM decision tree multi-class classification algorithm.In order to minimise the impact of the error accumulation to greatest extent, the algorithm uses the idea of vector projection as the standard to measure class separation, thus constructs an unbalanced decision tree.Furthermore, it selects different punishment factors from positive and negative samples at the nodes of decision tree to counteract the impact from unbalanced data sets.At last, it introduces KNN to co-recognise the data sets with SVM.Analysing and comparing diffident methods by the simulation experiment on handwritten digit recognition data sets, it is shown that this method can effectively improve the classification accuracy.关键词
支持向量机/多类分类/决策树/投影向量/惩罚因子/KNNKey words
Support vector machines( SVM)/Multi-class classification/Decision tree/Vector projection/Punishment factor k-nearest neighbour algorithm(KNN)分类
信息技术与安全科学引用本文复制引用
赵亮..一种改进的基于支持向量机的多类分类方法[J].计算机应用与软件,2014,(12):233-236,4.
