%0 Journal Article
	%A Talal Alshammari and  Nasser Alshammari and  Mohamed Sedky and  Chris Howard
	%D 2018
	%J International Journal of Information and Communication Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 134, 2018
	%T Evaluating Machine Learning Techniques for Activity Classification in Smart Home Environments
	%U https://publications.waset.org/pdf/10008539
	%V 134
	%X With the widespread adoption of the Internet-connected
devices, and with the prevalence of the Internet of Things (IoT)
applications, there is an increased interest in machine learning
techniques that can provide useful and interesting services in the
smart home domain. The areas that machine learning techniques
can help advance are varied and ever-evolving. Classifying smart
home inhabitants’ Activities of Daily Living (ADLs), is one
prominent example. The ability of machine learning technique to find
meaningful spatio-temporal relations of high-dimensional data is an
important requirement as well. This paper presents a comparative
evaluation of state-of-the-art machine learning techniques to classify
ADLs in the smart home domain. Forty-two synthetic datasets and
two real-world datasets with multiple inhabitants are used to evaluate
and compare the performance of the identified machine learning
techniques. Our results show significant performance differences
between the evaluated techniques. Such as AdaBoost, Cortical
Learning Algorithm (CLA), Decision Trees, Hidden Markov Model
(HMM), Multi-layer Perceptron (MLP), Structured Perceptron and
Support Vector Machines (SVM). Overall, neural network based
techniques have shown superiority over the other tested techniques.
	%P 72 - 78