Ulster University Logo

Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes

Jurek, Anna, Nugent, CD, Bi, Yaxin and Wu, Shengli (2014) Clustering-Based Ensemble Learning for Activity Recognition in Smart Homes. Sensors, 14 (7). p. 12285. [Journal article]

Full text not available from this repository.

URL: http://dx.doi.org/10.3390/s140712285

DOI: doi:10.3390/s140712285


Application of sensor-based technology within activity monitoring systems is becoming a popular technique within the smart environment paradigm. Nevertheless, the use of such an approach generates complex constructs of data, which subsequently requires the use of intricate activity recognition techniques to automatically infer the underlying activity. This paper explores a cluster-based ensemble method as a new solution for the purposes of activity recognition within smart environments. With this approach activities are modelled as collections of clusters built on different subsets of features. A classification process is performed by assigning a new instance to its closest cluster from each collection. Two different sensor data representations have been investigated, namely numeric and binary. Following the evaluation of the proposed methodology it has been demonstrated that the cluster-based ensemble method can be successfully applied as a viable option for activity recognition. Results following exposure to data collected from a range of activities indicated that the ensemble method had the ability to perform with accuracies of 94.2% and 97.5% for numeric and binary data, respectively. These results outperformed a range of single classifiers considered as benchmarks.

Item Type:Journal article
Faculties and Schools:Faculty of Computing & Engineering
Faculty of Computing & Engineering > School of Computing and Mathematics
Research Institutes and Groups:Computer Science Research Institute > Smart Environments
Computer Science Research Institute
Computer Science Research Institute > Artificial Intelligence and Applications
ID Code:30840
Deposited By: Professor Christopher Nugent
Deposited On:20 Jan 2015 14:40
Last Modified:20 Jan 2015 14:40

Repository Staff Only: item control page