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

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.