计算机工程与应用 ›› 2010, Vol. 46 ›› Issue (18): 130-131.DOI: 10.3778/j.issn.1002-8331.2010.18.041

• 数据库、信号与信息处理 • 上一篇    下一篇

结合属性值贡献度与平均相似度的KNN改进算法

张玲珠,周忠眉   

  1. 漳州师范学院 计算机科学与工程系,福建 漳州 363000

  • 收稿日期:2009-03-20 修回日期:2009-05-19 出版日期:2010-06-21 发布日期:2010-06-21
  • 通讯作者: 张玲珠

Improved KNN algorithm based on contribution of attribute value and average similarity

ZHANG Ling-zhu,ZHOU Zhong-mei   

  1. Department of Computer Science and Engineering,Zhangzhou Normal University,Zhangzhou,Fujian 363000,China

  • Received:2009-03-20 Revised:2009-05-19 Online:2010-06-21 Published:2010-06-21
  • Contact: ZHANG Ling-zhu

摘要:

与传统的K-近邻算法不同,提出了一种结合属性值贡献度与平均相似度的KNN改进算法。首先考虑测试样本与相似样本点间的平均相似度,其次考虑不同类别中的相似样本点的个数,最后还考虑与相似样本相同的属性值对类别的贡献度。在蘑菇数据集上进行实验结果表明,改进后的KNN分类算法的准确率比传统的K-近邻分类算法的准确率更高。

关键词: 分类, K-近邻算法, 相似度

Abstract: An improved KNN algorithm based on contribution of attribute value and average similarity is proposed,which is different from the traditional K-nearest neighbor algorithm.This paper not only considers the average similarity between the test samples and the similar samples but also considers the number of the similar samples in different categories.And finally considers the contribution of the same attribute value to the class label.The experiments is performed on mushroom data sets.The experimental results show that the approach has higher accuracy than the traditional K-nearest neighbor algorithm.

Key words: classification, K-Nearest Neighbor algorithm(KNN), similarity

中图分类号: