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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| [1] CHEN P,TU Y,TONG J,et al.Design of Wearable Fall Detection Intelligent System [J].Sensors and Microsystems,2017,36(2):114-116. [2] MUHAMMAD S,STEPHAN B,OZLEM D I,et al.A Survey of Online Activity Recognition Using Mobile Phones [J].Sensors,2015,15(1):2059-2085. [3] GOMES D,MENDES-MOREIRA J,SOUSA I,et al.Eating and Drinking Recognition in Free-Living Conditions for Triggering Smart Reminders[J].Sensors,2019,19(12):2803. [4] YUAN J G.Design and Implementation of Educational VRGames Based on UE4[C]//Southwest Petroleum University.Southwest Petroleum University,2019:249-254. [5] CERO D E,BARAKOVI H J,BARAKOVI S.Step by StepTowards Effective Human Activity Recognition:A Balance between Energy Consumption and Latency in Health and Wellbeing Applications[J].Sensors,2019,19(23):5206. [6] ZHENG Z W,DU J J,HUO M M,et al.Review of Research on Human Activity Recognition Based on Wearable Sensors [J].Computer Applications,2018,38(5):1223-1229. [7] SHARMILA A,GEETHANJALI P.Evaluation of Time Do-main Features Using Best Feature Subsets Based on Mutual Information For Detecting Epilepsy[J].Journal of Medical Engineering & Technology,2018,42(7):487-500. [8] AHMAD J,MUHAMMAD K,BAIK S W.Medical Image Retrieval with Compact Binary Codes Generated in Frequency Domain Using Highly Reactive Convolutional Features [J].Journal of Medical Systems,2017,42(2):3-24. [9] WU Q S,LIU C Y,LIN L H.Hybrid Feature Selection Algorithm Fusion Sequence Backward Selection and Support Vector Machine[J].Computer System Applications,2019,28 (7):174-179. [10] HUI H H,CHENG W H,MING D L.Hybrid Feature Selection by Combining Filters and Wrappers[J].Expert Systems with Applications,2011,38(7):8144-8150. [11] XIE J Y,XIE W X.Feature Selection Algorithm Based on Feature Subset Discrimination and Support Vector Machine [J].Chinese Journal of Computers,2014,37(8):1704-1718. [12] DAVIDE A,ALESSANDRO G,LUCA O,et al.A Public Domain Dataset for Human Activity Recognition Using Smartphones[C]//21th European Symposium on Artificial Neural Networks,Computational Intelligence and Machine Learning(ESANN 2013).Bruges,Belgium,2013:24-26. [13] HYUNKI L,DAE-W K.MFC:Initialization method for multi-label feature selection based on conditional mutual information[J].Neurocomputing,2019,22(6):1-12. [14] PENG X B,ZHU Y Q.Weighted SVM Algorithm Based on Intra-feature Correlation and Mutual Information [J].Computer Science,2018,45(12):182-186. [15] ZHANG L,WANG C.Multi-label Feature Selection Algorithm Based on Maximum Correlation and Minimum Redundant Joint Mutual Information [J].Journal of Communications,2018,39(5):111-122. [16] YANG Y J,LIU S,CHANG D X.A Method for Network Event Correlation Pattern Drift Detection Based on Relation Entropy and J Measure [J].Computer Science,2016,43(1):259-263. [17] YANG L,WANG Y.Review of Various Cross-Validation Estimation Methods for Generalized Errors [J].Application Research of Computers,2015,32(5):1287-1290. |
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