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.
|Publication status||Published - 8 Dec 2012|
|Event||Proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications - Cairo, Egypt|
Duration: 8 Dec 2012 → 10 Dec 2012
|Conference||Proceedings of the First International Conference on Advanced Machine Learning Technologies and Applications|
|Period||8/12/12 → 10/12/12|