Computer Engineering and Applications ›› 2012, Vol. 48 ›› Issue (33): 194-196.

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Research on character recognition based on Bagging ensemble learning

LIU Yuxia1, LV Hong1,2, HU Tao1, SUN Xiaohu1   

  1. 1.College of Electrical Engineering, Anhui Polytechnic University, Wuhu, Anhui 241000, China
    2.College of Electronic and Information Engineering, Anhui University of Architecture, Hefei 230022, China
  • Online:2012-11-21 Published:2012-11-20

基于Bagging集成学习的字符识别方法

刘余霞1,吕  虹1,2,胡  涛1,孙小虎1   

  1. 1.安徽工程大学 电气工程学院,安徽 芜湖 241000
    2.安徽建筑工业学院 电子与信息工程学院,合肥 230022

Abstract: Due to the diversity of character recognition, a character recognition model based on Bagging ensemble is presented, which solves recognition model’s preferences for certain character. Different datasets are formed by Bagging, and then base-classifier is constructed. Ensemble learning model is built by majority vote. Theoretic analysis and simulation result shows the model owns better classification accuracy than other classification methods.

Key words: Bagging, character recognition, ensemble learning, decision tree, Adaboost

摘要: 针对字符识别对象的多样性,提出了一种基于Bagging集成的字符识别模型,解决了识别模型对部分字符识别的偏好现象。采用Bagging采样策略形成不同的数据子集,在此基础上用决策树算法训练形成多个基分类器,用多数投票机制对基分类器预测结果集成输出。理论分析与仿真实验结果表明,所提模型相比其他分类方法具有更好的分类能力。

关键词: Bagging, 字符识别, 集成学习, 决策树, Adaboost