改进的一对一支持向量机多分类算法
Improved multi-classification algorithm of one-against-one SVM
查看参考文献15篇
文摘
|
支持向量机的一对一多分类算法具有良好的性能,但该算法在分类时存在不可分区域,影响了该方法的应用。因此,提出一种一对一与基于紧密度判决相结合的多分类方法,使用一对一算法分类,采用基于紧密度决策解决不可分区,依据样本到类中心之间的距离和基于kNN(k nearest neighbor)的样本分布情况结合的方式构建判别函数来确定类别归属。使用UCI(university of California Irvine)数据集做测试,测试结果表明,该算法能有效地解决不可分区域问题,而且表现出比其它算法更好的性能。 |
其他语种文摘
|
Multi-class classification algorithm of one-against-one SVM show good performance,but the algorithm exists an unclassifiable region,which affects the application effect of the algorithm.Hence,a multi-classification algorithm of integration of one-against-one and affinity decision is presented.Firstly,the one-against-one multi-class classification algorithm is used to classify samples,and then the affinity decision is used to solve samples in the unclassifiable region and to determine categories of samples,which using the approach of distance between the sample and centers of classes and sample distribution based on kNN(k nearest neighbor) to create decision function.By adopting UCI data sets for testing,the results show that the algorithm can solve unclassifiable region issues,and show better performance than other algorithms. |
来源
|
计算机工程与设计
,2012,33(5):1837-1841 【扩展库】
|
关键词
|
k近邻
;
一对一支持向量机
;
多分类
;
不可分区
;
紧密度
|
地址
|
1.
中国科学院沈阳自动化研究所, 辽宁, 沈阳, 110016
2.
北京联合大学管理学院, 北京, 100101
|
语种
|
中文 |
文献类型
|
研究性论文 |
ISSN
|
1000-7024 |
学科
|
自动化技术、计算机技术 |
基金
|
国家863计划
|
文献收藏号
|
CSCD:4535957
|
参考文献 共
15
共1页
|
1.
Vapnik V N.
Statistical learning theory,2009
|
被引
2
次
|
|
|
|
2.
Cui Jianguo. The application of support vector machine in pattern recognition.
2007 IEEE International Conference on Control and Automation,2008:3135-3138
|
被引
1
次
|
|
|
|
3.
Maenhout S. Support vector machine regression for the prediction of maize hybrid performance.
Theoretical and Applied Genetics,2007,115(7):1003-1013
|
被引
10
次
|
|
|
|
4.
苟博. 支持向量机多类分类方法.
数据采集与处理,2006,21(3):334-339
|
被引
29
次
|
|
|
|
5.
Takuya I. Fuzzy support vector machines for pattern classification.
Proceedings of International Joint Conference on Neural Networks,2001:1449-1454
|
被引
2
次
|
|
|
|
6.
Platt J C. Large margin DAGs for multi-class classification.
Advances in Neural Information Processing Systems,2000,12(3):547-553
|
被引
113
次
|
|
|
|
7.
刘波. 交互迭代一对一分类算法.
模式识别与人工智能,2008,21(4):425-431
|
被引
2
次
|
|
|
|
8.
王晓红. 一种新的多类支持向量机决策方法及其应用.
信息与控制,2008,37(6):647-652
|
被引
1
次
|
|
|
|
9.
孙吉贵. 聚类算法研究.
软件学报,2008,19(1):48-61
|
被引
452
次
|
|
|
|
10.
陶新民. 基于紧密度FSVM新算法及在故障检测中的应用.
振动工程学报,2009,22(4):418-424
|
被引
6
次
|
|
|
|
11.
Hao Zhifeng. A comparision of multiclass support vector machine algorithms.
Proceedings of the International Conference on Machine Learning and Cybernetics,2006:4221-4226
|
被引
1
次
|
|
|
|
12.
闫志刚. 多类支持向量机推广性能分析.
数据采集与处理,2009,24(4):469-475
|
被引
5
次
|
|
|
|
13.
Sambasivam S. Advanced data clustering methods of mining Web documents.
Issues in Informing Science and Information Technology,2006(3):563-579
|
被引
24
次
|
|
|
|
14.
Asuncion A.
UCI machine learning repository,2009
|
被引
10
次
|
|
|
|
15.
Hans-Peter K. Clustering high-dimensional data:A survey on subspace clustering, pattem-based clustering, and correlation clustering.
ACM Transactions on Knowledge Discovery from Data,2009,3(1):1-58
|
被引
3
次
|
|
|
|
|