Multiple Classifier System for Activity Recognition

  • Han, Yong-Koo (Department of Computer Engineering, Kyung Hee University) ;
  • Lee, Sung-Young (Department of Computer Engineering, Kyung Hee University) ;
  • Lee, young-Koo (Department of Computer Engineering, Kyung Hee University) ;
  • Lee, Jae-Won (School of Industrial Management, Korea University of Technology and Education)
  • Published : 2007.11.23

Abstract

Nowadays, activity recognition becomes a hot topic in context-aware computing. In activity recognition, machine learning techniques have been widely applied to learn the activity models from labeled activity samples. Most of the existing work uses only one learning method for activity learning and is focused on how to effectively utilize the labeled samples by refining the learning method. However, not much attention has been paid to the use of multiple classifiers for boosting the learning performance. In this paper, we use two methods to generate multiple classifiers. In the first method, the basic learning algorithms for each classifier are the same, while the training data is different (ASTD). In the second method, the basic learning algorithms for each classifier are different, while the training data is the same (ADTS). Experimental results indicate that ADTS can effectively improve activity recognition performance, while ASTD cannot achieve any improvement of the performance. We believe that the classifiers in ADTS are more diverse than those in ASTD.

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