计算机工程与应用 ›› 2018, Vol. 54 ›› Issue (6): 95-99.DOI: 10.3778/j.issn.1002-8331.1610-0109

• 模式识别与人工智能 • 上一篇    下一篇

邻域决策错误率的局部约简方法研究

王长宝1,杨习贝1,2,窦慧莉1,陈向坚1,王平心3   

  1. 1.江苏科技大学 计算机科学与工程学院,江苏 镇江 212003
    2.南京理工大学 经济管理学院,南京 210094
    3.江苏科技大学 数理学院,江苏 镇江 212003
  • 出版日期:2018-03-15 发布日期:2018-04-03

Research on local attribute reduction approach via neighborhood decision error rate

WANG Changbao1, YANG Xibei1,2, DOU Huili1, CHEN Xiangjian1, WANG Pingxin3   

  1. 1.School of Computer Science and Engineering, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
    2.School of Economics & Management, Nanjing University of Science and Technology, Nanjing 210094, China
    3.School of Mathematics and Physics, Jiangsu University of Science and Technology, Zhenjiang, Jiangsu 212003, China
  • Online:2018-03-15 Published:2018-04-03

摘要: 传统基于邻域决策错误率的属性约简准则是针对总体分类精度的提升而设计的,未能展现因约简而引起的各类别精度变化情况。针对这一问题,引入局部邻域决策错误率以及局部属性约简的概念,其目的是提升单个类别的分类精度。在此基础上,进一步给出了求解局部邻域决策错误率约简的启发式算法。在8个UCI数据集上的实验结果表明,局部约简不仅是提高各个类别精度的有效技术手段,而且也解决了因全局约简所引起的局部分类精度下降问题。

关键词: 属性约简, 全局约简, 启发式算法, 局部约简, 邻域粗糙集

Abstract: Traditional criteria of attribute reduction for neighborhood decision error rate is designed for improving overall classification accuracy, it does not take the variation of accuracy of each class into consideration when reduction finding is executed. From this point of view, the concepts of local neighborhood decision error rate and local attribute reduction are introduced for improving the classification accuracy of single class. Furthermore, a heuristic algorithm to compute local neighborhood decision error rate based reduction is presented. The experimental results on 8 UCI data sets show that the local reduction can not only improve the classification accuracy of single class, but also overcome the limitation of accuracy’s decreasing for single class, which may be caused by global reduction.

Key words: attribute reduction, global reduction, heuristic algorithm, local reduction, neighborhood rough set