多频段时域分解的行为识别特征优选方法

为了降低外界环境对移动用户行为识别的影响,保留行为敏感特征、提高行为识别的准确率,提出了一种多频段时域分解的人体行为识别特征优选方法。该方法对行为样本数据进行多频段分解,计算样本数据在不同频段信号的特征,利用遗传算法以决策树作为分类器进行特征优选,在多组特征中搜索出近似最优的特征组合。实验结果表明,该方法优选出的特征组合能有效提高行为识别的准确率。...

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Bibliographic Details
Published in计算机应用研究 Vol. 32; no. 7; pp. 1956 - 1958
Main Author 王忠民 王斌
Format Journal Article
LanguageChinese
Published 西安邮电大学计算机学院,西安,710121 2015
Subjects
决策树
多频段时域分解
特征优选
行为识别
遗传算法
特征优选
genetic algorithm
multiband time domain decomposition
遗传算法
decision tree
behavior recognition
行为识别
多频段时域分解
feature optimization
决策树
Online AccessGet full text
ISSN1001-3695
DOI10.3969/j.issn.1001-3695.2015.07.009

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Summary:为了降低外界环境对移动用户行为识别的影响,保留行为敏感特征、提高行为识别的准确率,提出了一种多频段时域分解的人体行为识别特征优选方法。该方法对行为样本数据进行多频段分解,计算样本数据在不同频段信号的特征,利用遗传算法以决策树作为分类器进行特征优选,在多组特征中搜索出近似最优的特征组合。实验结果表明,该方法优选出的特征组合能有效提高行为识别的准确率。
Bibliography:51-1196/TP
multiband time domain decomposition; behavior recognition; feature optimization; genetic algorithm; decision tree
In order to reduce the influence of the external environment to mobile user behavior recognition, select the sensitive features, and improve the accuracy of behavior recognition, this paper proposed an acceleration signal feature selection method to mobile user behavior recognition based on multiband time domain decomposition. In the method, it firstly decomposed the sample data to different time domain components. Then it computed the features of the different time domain components. Finally, it used the genetic algorithm and the decision tree as a classifier to choose the feature set which was sensitive to the human＇ s behaviors . Experiment results show that the multiband time domain decomposition feature optimization method can improve the accuracy of human behavior recognition effectively.
Wang Zhongmin, Wang Bin （School of Computer Science ＆ Technology, Xi＇ an. University of Posts ＆
ISSN:1001-3695
DOI:10.3969/j.issn.1001-3695.2015.07.009