基于改进特征子集区分度的行为识别特征选择方法  

Feature Selection Method for Behavior Recognition Based on Improved Feature Subset Discrimination

作　　者：王瑞杰 李军怀[1,2] 王侃 王怀军 商珣超[1] 徒鹏佳 WANG Rui-jie;LI Jun-huai;WANG Kan;WANG Huai-jun;SHANG Xun-chao;TU Peng-jia(School of Computer Science and Engineering,Xi’an University of Technology,Xi’an 710048,China;Shaanxi Key Laboratory for Network Computing and Security Technology,Xi’an 710048,China)

机构地区：[1]西安理工大学计算机科学与工程学院,西安710048 [2]陕西省网络计算与安全技术重点实验室,西安710048

出　　处：《计算机科学》2020年第S02期204-208,共5页Computer Science

基　　金：科技部重点研发计划(2017YFB1402103);国家自然科学基金(61971347);西安市科技计划项目(201805037YD15CG21(4))。

摘　　要：基于传感器的人体行为识别方法在健康监测、运动分析和人机交互等方面得到了广泛应用。特征选择是准确识别人体行为的关键环节,其目的是在提高分类性能的基础上从高维特征空间中筛选出与分类相关的特征,以降低特征维数和计算复杂度。然而,传统的特征选择方法面临着未考虑所选特征冗余性的挑战。因此,针对基于特征子集区分度(Discernibility of Feature Subsets,DFS)衡量准则的特征选择方法仅考虑多个特征的相关性而忽视特征之间冗余性对分类结果影响等缺陷,提出一种基于冗余性的特征子集区分度衡量准则的特征选择方法(Redundancy and Discernibility of Feature Subsets,R-DFS),在特征选择的过程引入冗余性分析,删除冗余特征,以提高分类准确率和降低计算复杂度。实验结果表明,改进方法可有效降低特征维数并提高分类准确度。Sensor-based human behavior recognition has been widely used in health monitoring,motion analysis and human-computer interaction.Feature selection acts a critical step when identifying human behaviors accurately,aiming to improve classification performance by selecting classification-related features,so as to reduce feature dimensions and computational complexity.The absence of feature redundancy,nevertheless,poses challenges to legacy feature selection methods.Therefore,to resolve the insufficiency that only feature correlation but not feature redundancy is involved in the Discernibility of Feature Subsets(DFS)-based feature selection method,a novel Redundancy and Discernibility of Feature Subsets(R-DFS)-based feature selection method is proposed to incorporate the redundancy analysis into feature selection process and remove redundant features,so as to improve classification accuracy rate and reduce computational complexity as well.Experimental results reveal that the improved method can efficiently reduce the feature dimension with the improved classification accuracy.

关 键 词：加速度传感器 行为识别 特征选择 特征子集区分度 特征相关性和冗余性 

分 类 号：TP3-05[自动化与计算机技术—计算机科学与技术]