Hybrid GA-PCA Feature Selection Approach for Inertial Human Activity Recognition

被引：0

作者：

El-Maaty, Aymara M. Abo ^{[1
]}

Wassal, Amr G. ^{[1
]}

机构：

[1] Cairo Univ, Comp Engn Dept, Gizah 12613, Egypt

来源：

2018 IEEE SYMPOSIUM SERIES ON COMPUTATIONAL INTELLIGENCE (IEEE SSCI) | 2018年

关键词：

Time-Series; Human Activity Recognition; Feature Selection; Evolutionary Algorithm; Genetic Algorithm; GENETIC ALGORITHMS;

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Genetic algorithms is used as a wrapper feature selection technique in many research studies. In this paper we investigate GA capabilities in selecting the best set of time-series features for human activity recognition application. We propose a hybrid GA-PCA approach, where GA is used to select a subset of N features from 561 features, then PCA is used to reduce the subset into M orthogonal features. Experimental results show the ability of GA to eliminate low performance features without affecting the classification accuracy.

引用

页码：1027 / 1032

页数：6

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