@inproceedings{8f6288fc54504c85ba12f8af25bc6b5d,
title = "Assessment of Feature Selection for Context Awareness RF Sensing Systems",
abstract = "Context awareness using Radio Frequency (RF) sensing systems has attracted increased attention due to its importance in future communication systems. In this work, we investigate the importance of multiple features in the classification of different RF human mobility scenarios. Considering a practical RF system operating in the 76-81 GHz band, we focus on different types of features computed from the RF time of flight. The importance of the features is then evaluated through its score computed with four methodologies: the Mutual Information (MI), the Analysis of Variance (ANOVA), the Recursive Feature Elimination (RFE), and the Feature Weighting algorithm (RelieF). The results indicate that the methodologies do not rank the multiple features coherently and some categories of features have lower importance. Moreover, when adopting a k-nearest Neighbor classifier the RFE methodology is shown to be advantageous as it achieves the highest accuracy with the smallest number of features.",
keywords = "Context Awareness, Feature Engineering, Machine Learning, RF Active Sensing",
author = "Ricardo Cruz and Antonio Furtado and Rodolfo Oliveira",
note = "Funding Information: ACKNOWLEDGEMENTS This work was supported by Funda{\c c}{\~a}o para a Ci{\^e}ncia e Tecnologia through the project RFSense under the grant UIDB/50008/2020. Publisher Copyright: {\textcopyright} 2022 IEEE.; 95th IEEE Vehicular Technology Conference - Spring, VTC 2022-Spring ; Conference date: 19-06-2022 Through 22-06-2022",
year = "2022",
month = jun,
doi = "10.1109/VTC2022-Spring54318.2022.9860673",
language = "English",
isbn = "978-1-6654-8244-8",
series = "IEEE Vehicular Technology Conference",
publisher = "Institute of Electrical and Electronics Engineers (IEEE)",
booktitle = "2022 IEEE 95th Vehicular Technology Conference",
address = "United States",
}