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交互迭代一对一分类算法 被引量：2

Alternating Iterative One-against-One Algorithm
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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.
作者 刘波 郝志峰 肖燕珊
机构地区 华南理工大学计算机科学与工程学院 Faculty of Information Technology 广州亚运会组委会信息技术部
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
分类号 TP181 [自动化与计算机技术—控制理论与控制工程]
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参考文献20

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同被引文献16

  • 1王欣,申世杰.木材无损检测研究概况与发展趋势[J].北京林业大学学报,2009,31(S1):202-205. 被引量：38
  • 2业宁,王厚立,徐兆军,丁建文.基于支持向量机的木材缺陷识别[J].计算机应用与软件,2006,23(4):3-5. 被引量：10
  • 3苟博,黄贤武.支持向量机多类分类方法[J].数据采集与处理,2006,21(3):334-339. 被引量：63
  • 4Pham D T, Muhamad Z, Mahmuddin M, et al. Using the bees al- gorithm to optimise a support vector machine for wood defect clas- sification[ C ]//Memofias del. Innovative Production Machines and Systems Virtual Conference. Cardiff: IPROMS, 2007. [ 2010- 07-06]. http ://repo.uum.edu.my/154/.
  • 5Gu I Y H, Andersson H, Vicen R. Wood defect classification based on image analysis and support vector machines [ J ]. Wood Science and Technology, 2010, 44(4) : 693-704.
  • 6Li G, Wang X, Feng H, et al. Analysis of wave velocity patterns in black cherry trees and its effect on internal decay detection [ J ]. Computers and Electronics in Agriculture, 2014, 104: 32-39.
  • 7Fan R E. Working set selection using the second order information for training SVM [ J ]. Journal of Machine Learning Research, 2005(6) : 1889-1918.
  • 8Collobert R, Bengio S. SVM Torch: a support vector machine for large-scale regression and classification problems [ J ]. Journal of Machine Learning Research, 2001 ( 1 ) : 143-160.
  • 9孙吉贵,刘杰,赵连宇.聚类算法研究[J].软件学报,2008(1):48-61. 被引量：1079
  • 10范宇,张冬妍,孙丽萍,徐宇.基于SVM的木材干燥过程含水率软测量研究[J].森林工程,2008,24(4):27-29. 被引量：8

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模式识别与人工智能
2008年 第4期

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