Pattara Aiyarak

Work place: Department of Computer Science, Faculty of Science Prince of Songkla University, Hat Yai, 90110, Thailand



Research Interests: Human-Computer Interaction, Computer systems and computational processes, Computational Learning Theory, Pattern Recognition


Pattara Aiyarak, is a computer and technology enthusiast. He received degrees in various fields such as bachelor’s degrees in economics, Banking and Finance, Physics and, of course, Ph.D. in Physics (Telecommunications). His background reflects his broad research interest in several areas. He recently works as an assistant professor in the department of Computer Science, Faculty of Science, Prince of Songkla University. He has supervised several postgraduate students in Forensic Science, MIS, Physics and Computer Science.

Author Articles
A Cyclic Attribution Technique Feature Selection Method for Human Activity Recognition

By Win Win Myo Wiphada Wettayaprasit Pattara Aiyarak

DOI:, Pub. Date: 8 Oct. 2019

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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