Abstract
In this work, we present a new classification model to solve Human Activity Recognition (HAR) problem. The new classifier is a hybrid of Random Tree and Monte-Carlo simulations where Random Tree is used to select random samples for each simulation. The simulation use a generative model to train a value function that predicts a activity depending on sensor values. The classifier trains in an unsupervised learning style and does not require a training example dataset. It builds value function depending on response from environment. The experiments are performed on HAR dataset and compared with the start-of-the-art rival techniques. The performance is measure using precision, recall, f-Score and accuracy rate. The results show the new algorithm performs better than its rival techniques in f-score and accuracy. The classifier is also scalable and can also generalize non-deterministic behaviours.
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Ahmed, I., Naveed, M., Adnan, M. (2019). A Novel Simulation Based Classifier Using Random Tree and Reinforcement Learning. In: Barolli, L., Xhafa, F., Khan, Z., Odhabi, H. (eds) Advances in Internet, Data and Web Technologies. EIDWT 2019. Lecture Notes on Data Engineering and Communications Technologies, vol 29. Springer, Cham. https://doi.org/10.1007/978-3-030-12839-5_36
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