TY - GEN
T1 - CBARS
T2 - Cluster based classification for activity recognition systems
AU - Abdallah, Zahraa S.
AU - Gaber, Mohamed Medhat
AU - Srinivasan, B
AU - Krishnaswamy, Shonali
PY - 2012
Y1 - 2012
N2 - Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.
AB - Activity recognition focuses on inferring current user activities by leveraging sensory data available on today’s sensor rich environment. Supervised learning has been applied pervasively for activity recognition. Typical activity recognition techniques process sensory data based on point-by-point approaches. In this paper, we propose a novel Cluster Based Classification for Activity Recognition Systems, CBARS. The novel approach processes activities as clusters to build a robust classification framework. CBARS integrates supervised, unsupervised and active learning and applies hybrid similarity measures technique for recognising activities. Extensive experimental results using real activity recognition dataset have evidenced that our new approach shows improved performance over other existing state-of-the-art learning methods.
UR - https://research.monash.edu/en/publications/9d81c3ff-be7f-427f-ae63-739cb5ed75a8
U2 - 10.1007/978-3-642-35326-0
DO - 10.1007/978-3-642-35326-0
M3 - Conference Contribution (Conference Proceeding)
SN - 9783642353260
SN - 9783642353253
BT - Advanced Machine Learning Technologies and Applications
ER -