An Efficient Feature Selection Method for Activity Classification
Shumei Zhang, Paul J. McCullagh, Vic Callaghan · 2014
Feature selection is a key step for activity classification applications. Feature selection selects the most relevant features and considers how to use each of the selected features in the most suitable format. This paper proposes an efficient feature selection method that organizes multiple subsets of features in a multilayer, rather than utilizing all selected features together as one large feature set. The proposed method was evaluated by 13 subjects (aged from 23 to 50) in a lab environment. The experimental results illustrate that the large number of features (3 vs. 7 features) are not associated with high classification accuracy using a single Support Vector Machine (SVM) model (61.3% vs. 44.7%). However, the accuracy was improved significantly (83.1% vs. 44.7%), when the selected 7 features were organized as 3 subsets and used to classify 10 postures (9 motionless with 1 motion) in 3 layers via a hierarchical algorithm, which combined a rule-based algorithm with 3 independent SVM models.