计算机与现代化 ›› 2014, Vol. 0 ›› Issue (1): 100-103,125.

• 算法设计与分析 • 上一篇    下一篇

基于误差模型的混合分类算法

  

  1. 抚顺职业技术学院信息工程系,辽宁抚顺113122
  • 收稿日期:2013-08-06 出版日期:2014-01-20 发布日期:2014-02-10
  • 作者简介: 丛雪燕(1974-),女,辽宁沈阳人,抚顺职业技术学院信息工程系副教授,硕士,研究方向:计算机软件与应用。

Error-based Hybrid Classification Algorithm

  1. Department of Information Engineering, Fushun Vocational Technology Institute, Fushun 113122, China
  • Received:2013-08-06 Online:2014-01-20 Published:2014-02-10

摘要: 针对目标变量为二进制的数据集合进行分类,提出一种新的基于误差模型的混合分类方法,可以提高分类的精度。采用实际数据集作为测试数据,结果表明本文提出的算法性能优于其他的混合算法以及现有的单一使用的分类方法,尤其是当2种方法预测不一致的比率较高时,利用该方法能够显著地改善预测的准确性。

关键词: 监督学习, 分类, 混合模型, 误差模型

Abstract:  A new error-based approach of hybrid classification is presented, when data sets with binary objective variables are classified and it could increase the accuracy of classification. The paper also uses data sets to test the proposed approach and compares with the single classification. The results show that this method greatly improve the property, especially when it is predicted by two methods and the rate of variance is higher, this hybrid approach had demonstrated impressive capacities to improve the prediction accuracy.

Key words: supervised learning, classification, hybrid model, error model