Many applications of human activity recognition like healthcare, security etc.
show how human activity recognition is important in everyday life. In this
paper, we compare different machine learning algorithms like Naïve Bayes
(NB), One R (1R) rule, Zero R (0R) rule, J 48 trees, Random Forest (RF)
and Random Tree (RT) applied on sensor-based human activity recognition
in a home environment. We show that Random Forest achieves better
performance in terms of correctly classified instances comparing to other
algorithms, while application of 0R rules algorithm achieves significantly
the worst performance. Additionally, in order to reduce the dimensionality
of the algorithm, we applied wrapper method using the same classifier in the
attribute selection. It is shown that using wrapper method the performance of
the classification in terms of correctly classified instances is not significantly
changed, while in terms of algorithm complexity shows much better performance.
After calculating accuracy of each algorithm, we calculate accuracy
for each activity classified by each classifier.
M. Marjanović Jakovljević, A. Njeguš, N. Dončov, “Data-Driven Human Activity Recognition in Smart Environments,” in Sinteza 2016 - International Scientific Conference on ICT and E-Business Related Research, Belgrade, Singidunum University, Serbia, 2016, pp. 94-99. doi:10.15308/Sinteza-2016-94-99
Marjanović Jakovljević, M., Njeguš, A., Dončov, N. (2016). Data-Driven Human Activity Recognition in Smart Environments. Paper presented at Sinteza 2016 - International Scientific Conference on ICT and E-Business Related Research. doi:10.15308/Sinteza-2016-94-99