Random Forest is an ensemble machine learning method that constructs multiple decision trees during training and aggregates their outputs — typically via majority voting for classification or averaging for regression — to produce a more robust and generalizable prediction than any single tree. In CSI-based Wi-Fi sensing, it is valued for its ability to handle high-dimensional, noisy channel state information features without extensive hyperparameter tuning, making it a practical baseline for tasks such as occupancy detection and passenger counting. Key variants relevant to the field include standard Random Forest classifiers used for discrete activity or occupancy classification, and regression-oriented formulations applied to continuous estimation tasks, both of which benefit from the method's inherent feature importance ranking, which aids in identifying the most discriminative CSI subcarriers or transceiver pairs.
Source Papers
- A Survey on Wireless Device-free Human Sensing: Application Scenarios, Current Solutions, and Open Issues ↗ — A Survey on Wireless Device-free Human Sensing: Application
- A survey on CSI-based Wi-Fi sensing datasets and models with a focus on reproducibility ↗ — A survey on CSI-based Wi-Fi sensing datasets and models with
- BLE Can See: A Reinforcement Learning Approach for RF-based Indoor Occupancy Detection ↗ — BLE Can See: A Reinforcement Learning Approach for RF-based
- Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground-Truth With Machine Learning ↗ — Bluetooth-Based Vehicle Counting: Bridging the Gap to Ground
- CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sensing ↗ — CSI-Chain: A Complete End-to-End Framework for WiFi CSI Sens
- CSI-based Passenger Counting on Public Transport Vehicles with Multiple Transceivers ↗ — CSI-based Passenger Counting on Public Transport Vehicles wi
- Channel State Information from Pure Communication to Sense and Track Human Motion: A Survey ↗ — Channel State Information from Pure Communication to Sense a
- CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing ↗ — CrossSense: Towards Cross-Site and Large-Scale WiFi Sensing
- Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensing in Stationary Crowd Counting ↗ — Guiding Wi-Fi Sensor Placement for Enhanced CSI-Based Sensin
- Human Activity Recognition via Wi-Fi and Inertial Sensors With Machine Learning ↗ — Human Activity Recognition via Wi-Fi and Inertial Sensors Wi
- Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions ↗ — Occupancy Prediction in IoT-Enabled Smart Buildings: Technol
- Towards Environment Independent Device Free Human Activity Recognition ↗ — Towards Environment Independent Device Free Human Activity R
- Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free Crowd Counting and Localization ↗ — Wi-CaL: WiFi Sensing and Machine Learning Based Device-Free
- WiFi-Based Human Sensing With Deep Learning: Recent Advances, Challenges, and Opportunities ↗ — WiFi-Based Human Sensing With Deep Learning: Recent Advances
- WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activity Sensing ↗ — WiMANS: A Benchmark Dataset for WiFi-based Multi-user Activi