A Novel Simulation Based Classifier Using Random Tree and Reinforcement Learning

  • Israr AhmedEmail author
  • Munir NaveedEmail author
  • Mohammed AdnanEmail author
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 29)


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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© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Higher Colleges of TechnologyAbu DhabiUnited Arab Emirates

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