摘要
在基于支持向量机的多分类算法中,一对一算法表现出较好的性能.然而此算法却存在不可分区域,落入该区域的样本不能有效被识别,因此影响了一对一算法的性能.为解决这个难题,提出交互迭代一对一分类算法,同时给出算法的有效性分析和计算复杂度证明.为了验证该算法解决不可分区域的能力,我们选用 UCI 数据集来做对比实验.实验结果显示,本文算法不但可以较成功解决不可分区域问题而且表现出比其它算法更好的性能.
One-against-one algorithm shows good performance in the multi-class classification algorithm based on SVMs. However, the existing middle unclassifiable region in the algorithm has a bad influence on its performance. To overcome this drawback, a method called aliernating iterative one-against-one algorithm is proposed. And the validity analysis and computational complexity of the proposed algorithm are presented. Finally, one-against-one, fuzzy support vector machine （FSVM）, decision directed aeyelie graph （DDAG） and the proposed algorithm are compared on UCI datasets. The experimental results show that the proposed algorithm resolves the unelassifiable region problem effectively and its performance is better than that of the others.
出处
《模式识别与人工智能》
EI
CSCD
北大核心
2008年第4期425-431,共7页
Pattern Recognition and Artificial Intelligence
基金
国家自然科学基金(No.10471045
10471045)
广东省自然科学基金(No.04020079
970472
000463
04020079)资助项目
关键词
支持向量机
多分类算法
一对一算法
模糊支持向量机
有向无环图算法
Support Vector Machine, Multi-Class Classification Algorithm, One-against-One Algorithm,Fuzzy Support Vector Machine, Decision Directed Acyclic Graph Algorithm
