计算机科学 ›› 2020, Vol. 47 ›› Issue (11A): 204-208.doi: 10.11896/jsjkx.200100030
王瑞杰1, 李军怀1,2, 王侃1,2, 王怀军1,2, 商珣超1, 徒鹏佳1
WANG Rui-jie1, LI Jun-huai1,2, WANG Kan1,2, WANG Huai-jun1,2, SHANG Xun-chao1, TU Peng-jia1
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。
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