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山东大学学报(工学版) ›› 2015, Vol. 45 ›› Issue (1): 13-18.doi: 10.6040/j.issn.1672-3961.1.2014.072

• 机器学习与数据挖掘 • 上一篇    下一篇

一种基于聚类的快速局部支持向量机算法

浩庆波1, 牟少敏1,2, 尹传环3, 昌腾腾1, 崔文斌1   

  1. 1. 山东农业大学信息科学与工程学院, 山东 泰安 271018;
    2. 山东农业大学农业大数据研究中心, 山东 泰安 271018;
    3. 北京交通大学计算机与信息技术学院, 北京 100044
  • 收稿日期:2014-03-26 修回日期:2015-01-08 出版日期:2015-02-20 发布日期:2014-03-26
  • 通讯作者: 牟少敏(1964-),男,山东泰安人,副教授,博士,主要研究方向为机器学习,模式识别,数字图像处理和信息安全.E-mail:msm@sdau.edu.cn E-mail:msm@sdau.edu.cn
  • 作者简介:浩庆波(1988-),男,山东泰安人,硕士研究生,主要研究方向为机器学习与数字图像处理.E-mail:haoqingbo4546@163.com
  • 基金资助:
    山东省自然科学基金资助项目(ZR2012FM024);国家自然科学青年基金资助项目(61105056);山东省农业重大应用技术创新课题资助项目

An algorithm of fast local support vector machine based on clustering

HAO Qingbo1, MU Shaomin1,2, YIN Chuanhuan3, CHANG Tengteng1, CUI Wenbin1   

  1. 1. School of Information Science and Engineering, Shandong Agricultural University, Taian 271018, Shandong, China;
    2. Agricultural Big-Data Research Center, Shandong Agricultural University, Taian 271018, Shandong, China;
    3. School of Computer and Information Technology, Beijing Jiaotong University, Beijing 100044, China
  • Received:2014-03-26 Revised:2015-01-08 Online:2015-02-20 Published:2014-03-26

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摘要： 为进一步改善局部支持向量机的分类效率和分类精度,提出一种改进的局部支持向量机算法。该算法对每类训练样本分别进行聚类,使用聚类生成的样本中心点集代替样本,使用改进的k最近邻算法选取测试样本的k个近邻。分别在UCI数据集和自建树皮图像数据集上对本研究算法的有效性进行测试。实验结果表明,本研究提出的算法在分类精度和效率上具有一定的优势。

关键词: k均值聚类, 分类, 局部支持向量机, 纹理特征, k最近邻, 核函数

Abstract: In order to further improve the classification efficiency and precision of local support vector machine, a new algorithm was proposed.The two major improvements were as follows. First, every type of training samples was clustered seperately, and the training samples were substituted for sample centers generated by clustering. Second, the k nearest neighbors of test samples were selected by using the improved k-nearest neighbor algorithm. Tests were done on UCI data sets and bark image data sets made by the proposed algorithm to verify its effectiveness. Experimental results demonstrated that this algorithm had certain superiority of classification accuracy and efficiency.

Key words: kernel function, local support vector machine, texture features, k-nearest neighbor, k-means clustering, classification

中图分类号: 

  • TP391
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