—Feature selection is a technique of selecting the most important features for predictive model construction. It is a key component in machine learning for many pattern recognition applications. The primary objective of this paper is to create a more precise system for Human Activity Recognition (HAR) by identifying the most appropriate features. We propose a Cyclic Attribution Technique (CAT) feature selection technique for recognition of human activity based on group theory and the fundamental properties of the cyclic group. We tested our model on UCI-HAR dataset focusing on six activities. With the proposed method, 561 features could be reduced to 63. Using an Artificial Neural Network (ANN), we compared performances of our new dataset with selected features and the original dataset classifier. Results showed that the model could provide an excellent overall accuracy of 96.7%. The proposed CAT technique can specify high-quality features to the success of HAR with ANN classifier. Two benefits support this technique by reducing classification overfitting and training time.
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A Cyclic Attribution Technique Feature Selection Method for Human Activity Recognition
Semantic Scholar · Computer Science · 2019
Abstract
—Feature selection is a technique of selecting the most important features for predictive model construction. It is a key component in machine learning for many pattern recognition applications. The primary objective of this paper is to create a more precise system for Human Activity Recognition (HAR) by identifying the most appropriate features. We propose a Cyclic Attribution Technique (CAT) feature selection technique for recognition of human activity based on group theory and the fundamental properties of the cyclic group. We tested our model on UCI-HAR dataset focusing on six activities. With the proposed method, 561 features could be reduced to 63. Using an Artificial Neural Network (ANN), we compared performances of our new dataset with selected features and the original dataset classifier. Results showed that the model could provide an excellent overall accuracy of 96.7%. The proposed CAT technique can specify high-quality features to the success of HAR with ANN classifier. Two benefits support this technique by reducing classification overfitting and training time.