多频段时域分解的行为识别特征优选方法  

Feature selection method for mobile user behavior recognition based on multiband time domain decomposition

作　　者：王忠民[1] 王斌[1] 

机构地区：[1]西安邮电大学计算机学院,西安710121

出　　处：《计算机应用研究》2015年第7期1956-1958,共3页Application Research of Computers

基　　金：国家自然科学基金资助项目(61373166);陕西省工业攻关项目(2012K06-05;2012NKC01-15);陕西省教育厅产业化培育项目(2012JC22)

摘　　要：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。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.

关 键 词：多频段时域分解 行为识别 特征优选 遗传算法 决策树 

分 类 号：TN911.7[电子电信—通信与信息系统]