A prediction classifier architecture to forecast device status on smart environments
Bruno S. C. M. Vilar, Cezar P. Schroeder, Cristina Wada, Rayanne H. Bezerra, Leonardo L. A. Heitzmann, Rafael Simionato, George D. C. Cavalcanti · 2016
In smart environments, the extraction of relevant information in large volumes of data collected from intelligent devices is a crucial issue. The extracted information can assist in automation of user activities and on daily chores, either suggesting or even changing the state of devices based on his/her routine. In this work, we propose a prediction architecture which combines an innovative preprocessing strategy with some well known classification algorithms for the environment automation. The preprocessing enhances the datasets by including features and organizing them in structures that improve the classification results. We verify which preprocessing parameters have significant impact on prediction performance using datasets collected from a real home equipped with sensors. In simulations, the avNNet, mlp and C5.0 classifiers attained the higher accuracies using Friedman and Nemenyi statistical tests, but none of them outperformed the others in all scenarios using this architecture.